question_id stringlengths 40 40 | question stringlengths 4 171 | answer sequence | evidence sequence |
|---|---|---|---|
753990d0b621d390ed58f20c4d9e4f065f0dc672 | What is the seed lexicon? | [
"a vocabulary of positive and negative predicates that helps determine the polarity score of an event",
"seed lexicon consists of positive and negative predicates"
] | [
[
"The seed lexicon consists of positive and negative predicates. If the predicate of an extracted event is in the seed lexicon and does not involve complex phenomena like negation, we assign the corresponding polarity score ($+1$ for positive events and $-1$ for negative events) to the event. We expect the mod... |
9d578ddccc27dd849244d632dd0f6bf27348ad81 | What are the results? | [
"Using all data to train: AL -- BiGRU achieved 0.843 accuracy, AL -- BERT achieved 0.863 accuracy, AL+CA+CO -- BiGRU achieved 0.866 accuracy, AL+CA+CO -- BERT achieved 0.835, accuracy, ACP -- BiGRU achieved 0.919 accuracy, ACP -- BERT achived 0.933, accuracy, ACP+AL+CA+CO -- BiGRU achieved 0.917 accuracy, ACP+AL+CA... | [
[
"As for ${\\rm Encoder}$, we compared two types of neural networks: BiGRU and BERT. GRU BIBREF16 is a recurrent neural network sequence encoder. BiGRU reads an input sequence forward and backward and the output is the concatenation of the final forward and backward hidden states.",
"We trained the model w... |
02e4bf719b1a504e385c35c6186742e720bcb281 | How are relations used to propagate polarity? | [
"based on the relation between events, the suggested polarity of one event can determine the possible polarity of the other event ",
"cause relation: both events in the relation should have the same polarity; concession relation: events should have opposite polarity"
] | [
[
"In this paper, we propose a simple and effective method for learning affective events that only requires a very small seed lexicon and a large raw corpus. As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates t... |
44c4bd6decc86f1091b5fc0728873d9324cdde4e | How big is the Japanese data? | [
"7000000 pairs of events were extracted from the Japanese Web corpus, 529850 pairs of events were extracted from the ACP corpus",
"The ACP corpus has around 700k events split into positive and negative polarity "
] | [
[
"As a raw corpus, we used a Japanese web corpus that was compiled through the procedures proposed by BIBREF13. To extract event pairs tagged with discourse relations, we used the Japanese dependency parser KNP and in-house postprocessing scripts BIBREF14. KNP used hand-written rules to segment each sentence i... |
86abeff85f3db79cf87a8c993e5e5aa61226dc98 | What are labels available in dataset for supervision? | [
"negative, positive"
] | [
[
"Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding affective events is important to various natural language pr... |
c029deb7f99756d2669abad0a349d917428e9c12 | How big are improvements of supervszed learning results trained on smalled labeled data enhanced with proposed approach copared to basic approach? | [
"3%"
] | [
[]
] |
39f8db10d949c6b477fa4b51e7c184016505884f | How does their model learn using mostly raw data? | [
"by exploiting discourse relations to propagate polarity from seed predicates to final sentiment polarity"
] | [
[
"In this paper, we propose a simple and effective method for learning affective events that only requires a very small seed lexicon and a large raw corpus. As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates t... |
d0bc782961567dc1dd7e074b621a6d6be44bb5b4 | How big is seed lexicon used for training? | [
"30 words"
] | [
[
"We constructed our seed lexicon consisting of 15 positive words and 15 negative words, as shown in Section SECREF27. From the corpus of about 100 million sentences, we obtained 1.4 millions event pairs for AL, 41 millions for CA, and 6 millions for CO. We randomly selected subsets of AL event pairs such that... |
a592498ba2fac994cd6fad7372836f0adb37e22a | How large is raw corpus used for training? | [
"100 million sentences"
] | [
[
"As a raw corpus, we used a Japanese web corpus that was compiled through the procedures proposed by BIBREF13. To extract event pairs tagged with discourse relations, we used the Japanese dependency parser KNP and in-house postprocessing scripts BIBREF14. KNP used hand-written rules to segment each sentence i... |
3a9d391d25cde8af3334ac62d478b36b30079d74 | Does the paper report macro F1? | [
"Yes",
"Yes"
] | [
[],
[
"We find that the multilingual model cannot handle infrequent categories, i.e., Awe/Sublime, Suspense and Humor. However, increasing the dataset with English data improves the results, suggesting that the classification would largely benefit from more annotated data. The best model overall is DBMDZ (.52... |
8d8300d88283c73424c8f301ad9fdd733845eb47 | How is the annotation experiment evaluated? | [
"confusion matrices of labels between annotators"
] | [
[
"We find that Cohen $\\kappa $ agreement ranges from .84 for Uneasiness in the English data, .81 for Humor and Nostalgia, down to German Suspense (.65), Awe/Sublime (.61) and Vitality for both languages (.50 English, .63 German). Both annotators have a similar emotion frequency profile, where the ranking is a... |
48b12eb53e2d507343f19b8a667696a39b719807 | What are the aesthetic emotions formalized? | [
"feelings of suspense experienced in narratives not only respond to the trajectory of the plot's content, but are also directly predictive of aesthetic liking (or disliking), Emotions that exhibit this dual capacity have been defined as “aesthetic emotions”"
] | [
[
"To emotionally move readers is considered a prime goal of literature since Latin antiquity BIBREF1, BIBREF2, BIBREF3. Deeply moved readers shed tears or get chills and goosebumps even in lab settings BIBREF4. In cases like these, the emotional response actually implies an aesthetic evaluation: narratives tha... |
003f884d3893532f8c302431c9f70be6f64d9be8 | Do they report results only on English data? | [
"No",
"Unanswerable"
] | [
[
"Our full dataset consists of all subreddits on Reddit from January 2013 to December 2014, for which there are at least 500 words in the vocabulary used to estimate our measures, in at least 4 months of the subreddit's history. We compute our measures over the comments written by users in a community in time ... |
bb97537a0a7c8f12a3f65eba73cefa6abcd2f2b2 | How do the various social phenomena examined manifest in different types of communities? | [
"Dynamic communities have substantially higher rates of monthly user retention than more stable communities. More distinctive communities exhibit moderately higher monthly retention rates than more generic communities. There is also a strong positive relationship between a community's dynamicity and the average num... | [
[
"We find that dynamic communities, such as Seahawks or starcraft, have substantially higher rates of monthly user retention than more stable communities (Spearman's INLINEFORM0 = 0.70, INLINEFORM1 0.001, computed with community points averaged over months; Figure FIGREF11 .A, left). Similarly, more distinctiv... |
eea089baedc0ce80731c8fdcb064b82f584f483a | What patterns do they observe about how user engagement varies with the characteristics of a community? | [
"communities that are characterized by specialized, constantly-updating content have higher user retention rates, but also exhibit larger linguistic gaps that separate newcomers from established members, within distinctive communities, established users have an increased propensity to engage with the community's sp... | [
[
"Engagement and community identity. We apply our framework to understand how two important aspects of user engagement in a community—the community's propensity to retain its users (Section SECREF3 ), and its permeability to new members (Section SECREF4 )—vary according to the type of collective identity it fo... |
edb2d24d6d10af13931b3a47a6543bd469752f0c | How did the select the 300 Reddit communities for comparison? | [
"They selected all the subreddits from January 2013 to December 2014 with at least 500 words in the vocabulary and at least 4 months of the subreddit's history. They also removed communities with the bulk of the contributions are in foreign language.",
"They collect subreddits from January 2013 to December 2014,2... | [
[
"Our full dataset consists of all subreddits on Reddit from January 2013 to December 2014, for which there are at least 500 words in the vocabulary used to estimate our measures, in at least 4 months of the subreddit's history. We compute our measures over the comments written by users in a community in time ... |
938cf30c4f1d14fa182e82919e16072fdbcf2a82 | How do the authors measure how temporally dynamic a community is? | [
"the average volatility of all utterances"
] | [
[
"Dynamicity. A highly dynamic community constantly shifts interests from one time window to another, and these temporal variations are reflected in its use of volatile language. Formally, we define the dynamicity of a community INLINEFORM0 as the average volatility of all utterances in INLINEFORM1 . We refer ... |
93f4ad6568207c9bd10d712a52f8de25b3ebadd4 | How do the authors measure how distinctive a community is? | [
" the average specificity of all utterances"
] | [
[
"Distinctiveness. A community with a very distinctive identity will tend to have distinctive interests, expressed through specialized language. Formally, we define the distinctiveness of a community INLINEFORM0 as the average specificity of all utterances in INLINEFORM1 . We refer to a community with a less d... |
71a7153e12879defa186bfb6dbafe79c74265e10 | What data is the language model pretrained on? | [
"Chinese general corpus",
"Unanswerable"
] | [
[
"To implement deep neural network models, we utilize the Keras library BIBREF36 with TensorFlow BIBREF37 backend. Each model is run on a single NVIDIA GeForce GTX 1080 Ti GPU. The models are trained by Adam optimization algorithm BIBREF38 whose parameters are the same as the default settings except for learni... |
85d1831c28d3c19c84472589a252e28e9884500f | What baselines is the proposed model compared against? | [
"BERT-Base, QANet",
"QANet BIBREF39, BERT-Base BIBREF26"
] | [
[
"Experimental Studies ::: Comparison with State-of-the-art Methods",
"Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT ... |
1959e0ebc21fafdf1dd20c6ea054161ba7446f61 | How is the clinical text structuring task defined? | [
"Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what th... | [
[
"Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or w... |
77cf4379106463b6ebcb5eb8fa5bb25450fa5fb8 | What are the specific tasks being unified? | [
" three types of questions, namely tumor size, proximal resection margin and distal resection margin",
"Unanswerable"
] | [
[
"To reduce the pipeline depth and break the barrier of non-uniform output formats, we present a question answering based clinical text structuring (QA-CTS) task (see Fig. FIGREF1). Unlike the traditional CTS task, our QA-CTS task aims to discover the most related text from original paragraph text. For some ca... |
06095a4dee77e9a570837b35fc38e77228664f91 | Is all text in this dataset a question, or are there unrelated sentences in between questions? | [
"the dataset consists of pathology reports including sentences and questions and answers about tumor size and resection margins so it does include additional sentences "
] | [
[
"Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of qu... |
19c9cfbc4f29104200393e848b7b9be41913a7ac | How many questions are in the dataset? | [
"2,714 "
] | [
[
"Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of qu... |
6743c1dd7764fc652cfe2ea29097ea09b5544bc3 | What is the perWhat are the tasks evaluated? | [
"Unanswerable"
] | [
[]
] |
14323046220b2aea8f15fba86819cbccc389ed8b | Are there privacy concerns with clinical data? | [
"Unanswerable"
] | [
[]
] |
08a5f8d36298b57f6a4fcb4b6ae5796dc5d944a4 | How they introduce domain-specific features into pre-trained language model? | [
"integrate clinical named entity information into pre-trained language model"
] | [
[
"We first present a question answering based clinical text structuring (QA-CTS) task, which unifies different specific tasks and make dataset shareable. We also propose an effective model to integrate clinical named entity information into pre-trained language model.",
"In this section, we present an effe... |
975a4ac9773a4af551142c324b64a0858670d06e | How big is QA-CTS task dataset? | [
"17,833 sentences, 826,987 characters and 2,714 question-answer pairs"
] | [
[
"Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of qu... |
326e08a0f5753b90622902bd4a9c94849a24b773 | How big is dataset of pathology reports collected from Ruijing Hospital? | [
"17,833 sentences, 826,987 characters and 2,714 question-answer pairs"
] | [
[
"Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of qu... |
bd78483a746fda4805a7678286f82d9621bc45cf | What are strong baseline models in specific tasks? | [
"state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26"
] | [
[
"Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computatio... |
dd155f01f6f4a14f9d25afc97504aefdc6d29c13 | What aspects have been compared between various language models? | [
"Quality measures using perplexity and recall, and performance measured using latency and energy usage. "
] | [
[
"For each model, we examined word-level perplexity, R@3 in next-word prediction, latency (ms/q), and energy usage (mJ/q). To explore the perplexity–recall relationship, we collected individual perplexity and recall statistics for each sentence in the test set."
]
] |
a9d530d68fb45b52d9bad9da2cd139db5a4b2f7c | what classic language models are mentioned in the paper? | [
"Kneser–Ney smoothing"
] | [
[
"In this paper, we examine the quality–performance tradeoff in the shift from non-neural to neural language models. In particular, we compare Kneser–Ney smoothing, widely accepted as the state of the art prior to NLMs, to the best NLMs today. The decrease in perplexity on standard datasets has been well docum... |
e07df8f613dbd567a35318cd6f6f4cb959f5c82d | What is a commonly used evaluation metric for language models? | [
"perplexity",
"perplexity"
] | [
[
"Deep learning has unquestionably advanced the state of the art in many natural language processing tasks, from syntactic dependency parsing BIBREF0 to named-entity recognition BIBREF1 to machine translation BIBREF2 . The same certainly applies to language modeling, where recent advances in neural language mo... |
1a43df221a567869964ad3b275de30af2ac35598 | Which dataset do they use a starting point in generating fake reviews? | [
"the Yelp Challenge dataset",
"Yelp Challenge dataset BIBREF2"
] | [
[
"We use the Yelp Challenge dataset BIBREF2 for our fake review generation. The dataset (Aug 2017) contains 2.9 million 1 –5 star restaurant reviews. We treat all reviews as genuine human-written reviews for the purpose of this work, since wide-scale deployment of machine-generated review attacks are not yet r... |
98b11f70239ef0e22511a3ecf6e413ecb726f954 | Do they use a pretrained NMT model to help generating reviews? | [
"No",
"No"
] | [
[],
[]
] |
d4d771bcb59bab4f3eb9026cda7d182eb582027d | How does using NMT ensure generated reviews stay on topic? | [
"Unanswerable"
] | [
[]
] |
12f1919a3e8ca460b931c6cacc268a926399dff4 | What kind of model do they use for detection? | [
"AdaBoost-based classifier"
] | [
[
"We developed an AdaBoost-based classifier to detect our new fake reviews, consisting of 200 shallow decision trees (depth 2). The features we used are recorded in Table~\\ref{table:features_adaboost} (Appendix)."
]
] |
cd1034c183edf630018f47ff70b48d74d2bb1649 | Does their detection tool work better than human detection? | [
"Yes"
] | [
[
"We noticed some variation in the detection of different fake review categories. The respondents in our MTurk survey had most difficulties recognizing reviews of category $(b=0.3, \\lambda=-5)$, where true positive rate was $40.4\\%$, while the true negative rate of the real class was $62.7\\%$. The precision... |
bd9930a613dd36646e2fc016b6eb21ab34c77621 | How many reviews in total (both generated and true) do they evaluate on Amazon Mechanical Turk? | [
"1,006 fake reviews and 994 real reviews"
] | [
[
"We first investigated overall detection of any NMT-Fake reviews (1,006 fake reviews and 994 real reviews). We found that the participants had big difficulties in detecting our fake reviews. In average, the reviews were detected with class-averaged \\emph{F-score of only 56\\%}, with 53\\% F-score for fake re... |
6e2ad9ad88cceabb6977222f5e090ece36aa84ea | Which baselines did they compare? | [
"The baseline model is a deep sequence-to-sequence encoder/decoder model with attention. The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism. The attention mechanism is computed as in BIBREF9 and we use a greedy search for decoding. W... | [
[
"We present in this section the baseline model from See et al. See2017 trained on the CNN/Daily Mail dataset. We reproduce the results from See et al. See2017 to then apply LRP on it.",
"The baseline model is a deep sequence-to-sequence encoder/decoder model with attention. The encoder is a bidirectional ... |
aacb0b97aed6fc6a8b471b8c2e5c4ddb60988bf5 | How many attention layers are there in their model? | [
"one"
] | [
[
"The baseline model is a deep sequence-to-sequence encoder/decoder model with attention. The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism. The attention mechanism is computed as in BIBREF9 and we use a greedy search for decod... |
710c1f8d4c137c8dad9972f5ceacdbf8004db208 | Is the explanation from saliency map correct? | [
"No"
] | [
[
"We showed that in some cases the saliency maps are truthful to the network's computation, meaning that they do highlight the input features that the network focused on. But we also showed that in some cases the saliency maps seem to not capture the important input features. This brought us to discuss the fac... |
47726be8641e1b864f17f85db9644ce676861576 | How is embedding quality assessed? | [
"We compare this method of bias mitigation with the no bias mitigation (\"Orig\"), geometric bias mitigation (\"Geo\"), the two pieces of our method alone (\"Prob\" and \"KNN\") and the composite method (\"KNN+Prob\"). We note that the composite method performs reasonably well according the the RIPA metric, and muc... | [
[
"We evaluate our framework on fastText embeddings trained on Wikipedia (2017), UMBC webbase corpus and statmt.org news dataset (16B tokens) BIBREF11. For simplicity, only the first 22000 words are used in all embeddings, though preliminary results indicate the findings extend to the full corpus. For our novel... |
8958465d1eaf81c8b781ba4d764a4f5329f026aa | What are the three measures of bias which are reduced in experiments? | [
"RIPA, Neighborhood Metric, WEAT"
] | [
[
"Geometric bias mitigation uses the cosine distances between words to both measure and remove gender bias BIBREF0. This method implicitly defines bias as a geometric asymmetry between words when projected onto a subspace, such as the gender subspace constructed from a set of gender pairs such as $\\mathcal {P... |
31b6544346e9a31d656e197ad01756813ee89422 | What are the probabilistic observations which contribute to the more robust algorithm? | [
"Unanswerable"
] | [
[]
] |
347e86893e8002024c2d10f618ca98e14689675f | What turn out to be more important high volume or high quality data? | [
"only high-quality data helps",
"high-quality"
] | [
[
"The Spearman $\\rho $ correlation for fastText models on the curated small dataset (clean), C1, improves the baselines by a large margin ($\\rho =0.354$ for Twi and 0.322 for Yorùbá) even with a small dataset. The improvement could be justified just by the larger vocabulary in Twi, but in the case of Yorùbá ... |
10091275f777e0c2890c3ac0fd0a7d8e266b57cf | How much is model improved by massive data and how much by quality? | [
"Unanswerable"
] | [
[]
] |
cbf1137912a47262314c94d36ced3232d5fa1926 | What two architectures are used? | [
"fastText, CWE-LP"
] | [
[
"As a first experiment, we compare the quality of fastText embeddings trained on (high-quality) curated data and (low-quality) massively extracted data for Twi and Yorùbá languages.",
"The huge ambiguity in the written Twi language motivates the exploration of different approaches to word embedding estima... |
519db0922376ce1e87fcdedaa626d665d9f3e8ce | Does this paper target European or Brazilian Portuguese? | [
"Unanswerable",
"Unanswerable"
] | [
[],
[]
] |
99a10823623f78dbff9ccecb210f187105a196e9 | What were the word embeddings trained on? | [
"large Portuguese corpus"
] | [
[
"In BIBREF14 , several word embedding models trained in a large Portuguese corpus are evaluated. Within the Word2Vec model, two training strategies were used. In the first, namely Skip-Gram, the model is given the word and attempts to predict its neighboring words. The second, Continuous Bag-of-Words (CBOW), ... |
09f0dce416a1e40cc6a24a8b42a802747d2c9363 | Which word embeddings are analysed? | [
"Continuous Bag-of-Words (CBOW)"
] | [
[
"In BIBREF14 , several word embedding models trained in a large Portuguese corpus are evaluated. Within the Word2Vec model, two training strategies were used. In the first, namely Skip-Gram, the model is given the word and attempts to predict its neighboring words. The second, Continuous Bag-of-Words (CBOW), ... |
ac706631f2b3fa39bf173cd62480072601e44f66 | Did they experiment on this dataset? | [
"No",
"Yes"
] | [
[],
[
"In order to obtain the citation data of the Czech apex courts, it was necessary to recognize and extract the references from the CzCDC 1.0. Given that training data for both the reference recognition model BIBREF13, BIBREF34 and the text segmentation model BIBREF33 are publicly available, we were able ... |
8b71ede8170162883f785040e8628a97fc6b5bcb | How is quality of the citation measured? | [
"it is necessary to evaluate the performance of the above mentioned part of the pipeline before proceeding further. The evaluation of the performance is summarised in Table TABREF11. It shows that organising the two models into the pipeline boosted the performance of the reference recognition model, leading to a hi... | [
[
"In order to obtain the citation data of the Czech apex courts, it was necessary to recognize and extract the references from the CzCDC 1.0. Given that training data for both the reference recognition model BIBREF13, BIBREF34 and the text segmentation model BIBREF33 are publicly available, we were able to con... |
fa2a384a23f5d0fe114ef6a39dced139bddac20e | How big is the dataset? | [
"903019 references"
] | [
[
"Overall, through the process described in Section SECREF3, we have retrieved three datasets of extracted references - one dataset per each of the apex courts. These datasets consist of the individual pairs containing the identification of the decision from which the reference was retrieved, and the identific... |
53712f0ce764633dbb034e550bb6604f15c0cacd | Do they evaluate only on English datasets? | [
"Unanswerable"
] | [
[]
] |
0bffc3d82d02910d4816c16b390125e5df55fd01 | Do the authors mention any possible confounds in this study? | [
"No"
] | [
[]
] |
bdd8368debcb1bdad14c454aaf96695ac5186b09 | How is the intensity of the PTSD established? | [
"Given we have four intensity, No PTSD, Low Risk PTSD, Moderate Risk PTSD and High Risk PTSD with a score of 0, 1, 2 and 3 respectively, the estimated intensity is established as mean squared error.",
"defined into four categories from high risk, moderate risk, to low risk"
] | [
[
"To provide an initial results, we take 50% of users' last week's (the week they responded of having PTSD) data to develop PTSD Linguistic dictionary and apply LAXARY framework to fill up surveys on rest of 50% dataset. The distribution of this training-test dataset segmentation followed a 50% distribution of... |
3334f50fe1796ce0df9dd58540e9c08be5856c23 | How is LIWC incorporated into this system? | [
" For each user, we calculate the proportion of tweets scored positively by each LIWC category.",
"to calculate the possible scores of each survey question using PTSD Linguistic Dictionary "
] | [
[
"A threshold of 1 for $s-score$ divides scores into positive and negative classes. In a multi-class setting, the algorithm minimizes the cross entropy, selecting the model with the highest probability. For each user, we calculate the proportion of tweets scored positively by each LIWC category. These proporti... |
7081b6909cb87b58a7b85017a2278275be58bf60 | How many twitter users are surveyed using the clinically validated survey? | [
"210"
] | [
[
"We download 210 users' all twitter posts who are war veterans and clinically diagnosed with PTSD sufferers as well which resulted a total 12,385 tweets. Fig FIGREF16 shows each of the 210 veteran twitter users' monthly average tweets. We categorize these Tweets into two groups: Tweets related to work and Twe... |
1870f871a5bcea418c44f81f352897a2f53d0971 | Which clinically validated survey tools are used? | [
"DOSPERT, BSSS and VIAS"
] | [
[
"We use an automated regular expression based searching to find potential veterans with PTSD in twitter, and then refine the list manually. First, we select different keywords to search twitter users of different categories. For example, to search self-claimed diagnosed PTSD sufferers, we select keywords rela... |
ce6201435cc1196ad72b742db92abd709e0f9e8d | Did they experiment with the dataset? | [
"Yes"
] | [
[
"In Figure FIGREF28, we show some examples of the annotation results in CORD-19-NER. We can see that our distantly- or weakly supervised methods achieve high quality recognizing the new entity types, requiring only several seed examples as the input. For example, we recognized “SARS-CoV-2\" as the “CORONAVIRU... |
928828544e38fe26c53d81d1b9c70a9fb1cc3feb | What is the size of this dataset? | [
"29,500 documents",
"29,500 documents in the CORD-19 corpus (2020-03-13)"
] | [
[
"Named entity recognition (NER) is a fundamental step in text mining system development to facilitate the COVID-19 studies. There is critical need for NER methods that can quickly adapt to all the COVID-19 related new types without much human effort for training data annotation. We created this CORD-19-NER da... |
4f243056e63a74d1349488983dc1238228ca76a7 | Do they list all the named entity types present? | [
"No"
] | [
[]
] |
8f87215f4709ee1eb9ddcc7900c6c054c970160b | how is quality measured? | [
"Accuracy and the macro-F1 (averaged F1 over positive and negative classes) are used as a measure of quality.",
"Unanswerable"
] | [
[],
[]
] |
b04098f7507efdffcbabd600391ef32318da28b3 | how many languages exactly is the sentiment lexica for? | [
"Unanswerable"
] | [
[]
] |
8fc14714eb83817341ada708b9a0b6b4c6ab5023 | what sentiment sources do they compare with? | [
"manually created lexicon in Czech BIBREF11 , German BIBREF12 , French BIBREF13 , Macedonian BIBREF14 , and Spanish BIBREF15"
] | [
[
"As the gold standard sentiment lexica, we chose manually created lexicon in Czech BIBREF11 , German BIBREF12 , French BIBREF13 , Macedonian BIBREF14 , and Spanish BIBREF15 . These lexica contain general domain words (as opposed to Twitter or Bible). As gold standard for twitter domain we use emoticon dataset... |
d94ac550dfdb9e4bbe04392156065c072b9d75e1 | Is the method described in this work a clustering-based method? | [
"Yes",
"Yes"
] | [
[
"The task of Word Sense Induction (WSI) can be seen as an unsupervised version of WSD. WSI aims at clustering word senses and does not require to map each cluster to a predefined sense. Instead of that, word sense inventories are induced automatically from the clusters, treating each cluster as a single sense... |
eeb6e0caa4cf5fdd887e1930e22c816b99306473 | How are the different senses annotated/labeled? | [
"The contexts are manually labelled with WordNet senses of the target words"
] | [
[
"The dataset consists of a set of polysemous words: 20 nouns, 20 verbs, and 10 adjectives and specifies 20 to 100 contexts per word, with the total of 4,664 contexts, drawn from the Open American National Corpus. Given a set of contexts of a polysemous word, the participants of the competition had to divide t... |
3c0eaa2e24c1442d988814318de5f25729696ef5 | Was any extrinsic evaluation carried out? | [
"Yes"
] | [
[
"We first evaluate our converted embedding models on multi-language lexical similarity and relatedness tasks, as a sanity check, to make sure the word sense induction process did not hurt the general performance of the embeddings. Then, we test the sense embeddings on WSD task."
]
] |
dc1fe3359faa2d7daa891c1df33df85558bc461b | Does the model use both spectrogram images and raw waveforms as features? | [
"No"
] | [
[]
] |
922f1b740f8b13fdc8371e2a275269a44c86195e | Is the performance compared against a baseline model? | [
"Yes",
"No"
] | [
[
"In Table TABREF1, we summarize the quantitative results of the above previous studies. It includes the model basis, feature description, languages classified and the used dataset along with accuracy obtained. The table also lists the overall results of our proposed models (at the top). The languages used by ... |
b39f2249a1489a2cef74155496511cc5d1b2a73d | What is the accuracy reported by state-of-the-art methods? | [
"Answer with content missing: (Table 1)\nPrevious state-of-the art on same dataset: ResNet50 89% (6 languages), SVM-HMM 70% (4 languages)"
] | [
[
"In Table TABREF1, we summarize the quantitative results of the above previous studies. It includes the model basis, feature description, languages classified and the used dataset along with accuracy obtained. The table also lists the overall results of our proposed models (at the top). The languages used by ... |
591231d75ff492160958f8aa1e6bfcbbcd85a776 | Which vision-based approaches does this approach outperform? | [
"CNN-mean, CNN-avgmax"
] | [
[
"We compare our approach with two baseline vision-based methods proposed in BIBREF6 , BIBREF7 , which measure the similarity of two sets of global visual features for bilingual lexicon induction:",
"CNN-mean: taking the similarity score of the averaged feature of the two image sets.",
"CNN-avgmax: tak... |
9e805020132d950b54531b1a2620f61552f06114 | What baseline is used for the experimental setup? | [
"CNN-mean, CNN-avgmax"
] | [
[
"We compare our approach with two baseline vision-based methods proposed in BIBREF6 , BIBREF7 , which measure the similarity of two sets of global visual features for bilingual lexicon induction:",
"CNN-mean: taking the similarity score of the averaged feature of the two image sets.",
"CNN-avgmax: tak... |
95abda842c4df95b4c5e84ac7d04942f1250b571 | Which languages are used in the multi-lingual caption model? | [
"German-English, French-English, and Japanese-English",
"multiple language pairs including German-English, French-English, and Japanese-English."
] | [
[
"We carry out experiments on multiple language pairs including German-English, French-English, and Japanese-English. The experimental results show that the proposed multi-lingual caption model not only achieves better caption performance than independent mono-lingual models for data-scarce languages, but also... |
2419b38624201d678c530eba877c0c016cccd49f | Did they experiment on all the tasks? | [
"Yes"
] | [
[
"Implementation & Models Parameters. For all our tasks, we use the BERT-Base Multilingual Cased model released by the authors . The model is trained on 104 languages (including Arabic) with 12 layer, 768 hidden units each, 12 attention heads, and has 110M parameters in entire model. The model has 119,547 shar... |
b99d100d17e2a121c3c8ff789971ce66d1d40a4d | What models did they compare to? | [
" we do not explicitly compare to previous research since most existing works either exploit smaller data (and so it will not be a fair comparison), use methods pre-dating BERT (and so will likely be outperformed by our models)"
] | [
[
"Although we create new models for tasks such as sentiment analysis and gender detection as part of AraNet, our focus is more on putting together the toolkit itself and providing strong baselines that can be compared to. Hence, although we provide some baseline models for some of the tasks, we do not explicit... |
578d0b23cb983b445b1a256a34f969b34d332075 | What datasets are used in training? | [
"Arap-Tweet BIBREF19 , an in-house Twitter dataset for gender, the MADAR shared task 2 BIBREF20, the LAMA-DINA dataset from BIBREF22, LAMA-DIST, Arabic tweets released by IDAT@FIRE2019 shared-task BIBREF24, BIBREF25, BIBREF26, BIBREF27, BIBREF1, BIBREF28, BIBREF29, BIBREF30, BIBREF31, BIBREF32, BIBREF33, BIBREF34",... | [
[
"Arab-Tweet. For modeling age and gender, we use Arap-Tweet BIBREF19 , which we will refer to as Arab-Tweet. Arab-tweet is a tweet dataset of 11 Arabic regions from 17 different countries. For each region, data from 100 Twitter users were crawled. Users needed to have posted at least 2,000 and were selected b... |
6548db45fc28e8a8b51f114635bad14a13eaec5b | Which GAN do they use? | [
"We construct a GAN model which combines different sets of word embeddings INLINEFORM4 , INLINEFORM5 , into a single set of word embeddings INLINEFORM6 . ",
"weGAN, deGAN"
] | [
[
"We assume that for each corpora INLINEFORM0 , we are given word embeddings for each word INLINEFORM1 , where INLINEFORM2 is the dimension of each word embedding. We are also given a classification task on documents that is represented by a parametric model INLINEFORM3 taking document embeddings as feature ve... |
4c4f76837d1329835df88b0921f4fe8bda26606f | Do they evaluate grammaticality of generated text? | [
"No"
] | [
[
"We hypothesize that because weGAN takes into account document labels in a semi-supervised way, the embeddings trained from weGAN can better incorporate the labeling information and therefore, produce document embeddings which are better separated. The results are shown in Table 1 and averaged over 5 randomiz... |
819d2e97f54afcc7cdb3d894a072bcadfba9b747 | Which corpora do they use? | [
"CNN, TIME, 20 Newsgroups, and Reuters-21578"
] | [
[
"In the experiments, we consider four data sets, two of them newly created and the remaining two already public: CNN, TIME, 20 Newsgroups, and Reuters-21578. The code and the two new data sets are available at github.com/baiyangwang/emgan. For the pre-processing of all the documents, we transformed all charac... |
637aa32a34b20b4b0f1b5dfa08ef4e0e5ed33d52 | Do they report results only on English datasets? | [
"Yes"
] | [
[
"Even though this corpus has incorrect sentences and their emotional labels, they lack their respective corrected sentences, necessary for the training of our model. In order to obtain this missing information, we outsource native English speakers from an unbiased and anonymous platform, called Amazon Mechani... |
4b8257cdd9a60087fa901da1f4250e7d910896df | How do the authors define or exemplify 'incorrect words'? | [
"typos in spellings or ungrammatical words"
] | [
[
"Understanding a user's intent and sentiment is of utmost importance for current intelligent chatbots to respond appropriately to human requests. However, current systems are not able to perform to their best capacity when presented with incomplete data, meaning sentences with missing or incorrect words. This... |
7e161d9facd100544fa339b06f656eb2fc64ed28 | How many vanilla transformers do they use after applying an embedding layer? | [
"Unanswerable"
] | [
[]
] |
abc5836c54fc2ac8465aee5a83b9c0f86c6fd6f5 | Do they test their approach on a dataset without incomplete data? | [
"No",
"No"
] | [
[
"The incomplete dataset used for training is composed of lower-cased incomplete data obtained by manipulating the original corpora. The incomplete sentences with STT error are obtained in a 2-step process shown in Fig. FIGREF22. The first step is to apply a TTS module to the available complete sentence. Here,... |
4debd7926941f1a02266b1a7be2df8ba6e79311a | Should their approach be applied only when dealing with incomplete data? | [
"No",
"No"
] | [
[
"Experimental results for the Twitter Sentiment Classification task on Kaggle's Sentiment140 Corpus dataset, displayed in Table TABREF37, show that our model has better F1-micros scores, outperforming the baseline models by 6$\\%$ to 8$\\%$. We evaluate our model and baseline models on three versions of the d... |
3b745f086fb5849e7ce7ce2c02ccbde7cfdedda5 | By how much do they outperform other models in the sentiment in intent classification tasks? | [
"In the sentiment classification task by 6% to 8% and in the intent classification task by 0.94% on average"
] | [
[
"Experimental results for the Twitter Sentiment Classification task on Kaggle's Sentiment140 Corpus dataset, displayed in Table TABREF37, show that our model has better F1-micros scores, outperforming the baseline models by 6$\\%$ to 8$\\%$. We evaluate our model and baseline models on three versions of the d... |
44c7c1fbac80eaea736622913d65fe6453d72828 | What is the sample size of people used to measure user satisfaction? | [
"34,432 user conversations",
"34,432 "
] | [
[
"From January 5, 2019 to March 5, 2019, we collected conversational data for Gunrock. During this time, no other code updates occurred. We analyzed conversations for Gunrock with at least 3 user turns to avoid conversations triggered by accident. Overall, this resulted in a total of 34,432 user conversations.... |
3e0c9469821cb01a75e1818f2acb668d071fcf40 | What are all the metrics to measure user engagement? | [
"overall rating, mean number of turns",
"overall rating, mean number of turns"
] | [
[
"We assessed the degree of conversational depth by measuring users' mean word count. Prior work has found that an increase in word count has been linked to improved user engagement (e.g., in a social dialog system BIBREF13). For each user conversation, we extracted the overall rating, the number of turns of t... |
a725246bac4625e6fe99ea236a96ccb21b5f30c6 | What the system designs introduced? | [
"Amazon Conversational Bot Toolkit, natural language understanding (NLU) (nlu) module, dialog manager, knowledge bases, natural language generation (NLG) (nlg) module, text to speech (TTS) (tts)"
] | [
[
"Figure FIGREF3 provides an overview of Gunrock's architecture. We extend the Amazon Conversational Bot Toolkit (CoBot) BIBREF6 which is a flexible event-driven framework. CoBot provides ASR results and natural language processing pipelines through the Alexa Skills Kit (ASK) BIBREF7. Gunrock corrects ASR acco... |
516626825e51ca1e8a3e0ac896c538c9d8a747c8 | Do they specify the model they use for Gunrock? | [
"No"
] | [
[
"Figure FIGREF3 provides an overview of Gunrock's architecture. We extend the Amazon Conversational Bot Toolkit (CoBot) BIBREF6 which is a flexible event-driven framework. CoBot provides ASR results and natural language processing pipelines through the Alexa Skills Kit (ASK) BIBREF7. Gunrock corrects ASR acco... |
77af93200138f46bb178c02f710944a01ed86481 | Do they gather explicit user satisfaction data on Gunrock? | [
"Yes"
] | [
[
"From January 5, 2019 to March 5, 2019, we collected conversational data for Gunrock. During this time, no other code updates occurred. We analyzed conversations for Gunrock with at least 3 user turns to avoid conversations triggered by accident. Overall, this resulted in a total of 34,432 user conversations.... |
71538776757a32eee930d297f6667cd0ec2e9231 | How do they correlate user backstory queries to user satisfaction? | [
"modeled the relationship between word count and the two metrics of user engagement (overall rating, mean number of turns) in separate linear regressions"
] | [
[
"We assessed the degree of conversational depth by measuring users' mean word count. Prior work has found that an increase in word count has been linked to improved user engagement (e.g., in a social dialog system BIBREF13). For each user conversation, we extracted the overall rating, the number of turns of t... |
830de0bd007c4135302138ffa8f4843e4915e440 | Do the authors report only on English? | [
"Unanswerable"
] | [
[]
] |
680dc3e56d1dc4af46512284b9996a1056f89ded | What is the baseline for the experiments? | [
"FastText, BiLSTM, BERT",
"FastText, BERT , two-layer BiLSTM architecture with GloVe word embeddings"
] | [
[
"FastTextBIBREF4: It uses bag of words and bag of n-grams as features for text classification, capturing partial information about the local word order efficiently.",
"BiLSTM: Unlike feedforward neural networks, recurrent neural networks like BiLSTMs use memory based on history information to learn long-d... |
bd5379047c2cf090bea838c67b6ed44773bcd56f | Which experiments are perfomed? | [
"They used BERT-based models to detect subjective language in the WNC corpus"
] | [
[
"In natural language, subjectivity refers to the aspects of communication used to express opinions, evaluations, and speculationsBIBREF0, often influenced by one's emotional state and viewpoints. Writers and editors of texts like news and textbooks try to avoid the use of biased language, yet subjective bias ... |
7aa8375cdf4690fc3b9b1799b0f5a9ec1c1736ed | Is ROUGE their only baseline? | [
"No",
"No, other baseline metrics they use besides ROUGE-L are n-gram overlap, negative cross-entropy, perplexity, and BLEU."
] | [
[
"Our first baseline is ROUGE-L BIBREF1 , since it is the most commonly used metric for compression tasks. ROUGE-L measures the similarity of two sentences based on their longest common subsequence. Generated and reference compressions are tokenized and lowercased. For multiple references, we only make use of ... |
3ac30bd7476d759ea5d9a5abf696d4dfc480175b | what language models do they use? | [
"LSTM LMs"
] | [
[
"We calculate the probability of a sentence with a long-short term memory (LSTM, hochreiter1997long) LM, i.e., a special type of RNN LM, which has been trained on a large corpus. More details on LSTM LMs can be found, e.g., in sundermeyer2012lstm. The unigram probabilities for SLOR are estimated using the sam... |
0e57a0983b4731eba9470ba964d131045c8c7ea7 | what questions do they ask human judges? | [
"Unanswerable"
] | [
[]
] |
f0317e48dafe117829e88e54ed2edab24b86edb1 | What misbehavior is identified? | [
"if the attention loose track of the objects in the picture and \"gets lost\", the model still takes it into account and somehow overrides the information brought by the text-based annotations",
"if the attention loose track of the objects in the picture and \"gets lost\", the model still takes it into account an... | [
[
"It is also worth to mention that we use a ResNet trained on 1.28 million images for a classification tasks. The features used by the attention mechanism are strongly object-oriented and the machine could miss important information for a multimodal translation task. We believe that the robust architecture of ... |
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