question stringlengths 21 162 | answer stringlengths 1 892 | pos_doc_ids listlengths 1 14 |
|---|---|---|
which multilingual approaches do they compare with? | BIBREF19, BIBREF20 | [
22
] |
what are the pivot-based baselines? | pivoting, pivoting$_{\rm m}$ | [
22,
25
] |
which datasets did they experiment with? | Europarl, MultiUN | [
17
] |
what language pairs are explored? | De-En, En-Fr, Fr-En, En-Es, Ro-En, En-De, Ar-En, En-Ru | [
18,
19
] |
what ner models were evaluated? | Stanford NER, spaCy 2.0 , recurrent model with a CRF top layer | [
46,
47,
48
] |
what is the source of the news sentences? | ilur.am | [
42
] |
what are the topics pulled from Reddit? | politics, business, science, and AskReddit, and 1000 additional posts from the Reddit frontpage. | [
57
] |
What predictive model do they build? | logistic regression models | [
70
] |
What crowdsourcing platform is used? | They did not use any platform, instead they hired undergraduate students to do the annotation. | [
119
] |
How do they match words before reordering them? | CFILT-preorder system | [
146
] |
On how many language pairs do they show that preordering assisting language sentences helps translation quality? | 5 | [
143
] |
Which dataset(s) do they experiment with? | IITB English-Hindi parallel corpus BIBREF22, ILCI English-Hindi parallel corpus | [
143
] |
Which information about text structure is included in the corpus? | paragraphs, lines, Information on physical page segmentation (for PDFs only), paragraph segmentation, and line segmentation | [
152,
162
] |
Which information about typography is included in the corpus? | font type, font style, Information on the font type and font style (e.g., italics, bold print) of a token and its position on the physical page | [
152,
162
] |
On which benchmarks they achieve the state of the art? | SimpleQuestions, WebQSP | [
203
] |
What they use in their propsoed framework? | break the relation names into word sequences, relation-level and word-level relation representations, bidirectional LSTMs (BiLSTMs), residual learning method | [
171
] |
What does KBQA abbreviate for | Knowledge Base Question Answering | [
168
] |
What is te core component for KBQA? | answer questions by obtaining information from KB tuples | [
168
] |
What is the performance of BERT on the task? | F1 scores are:
HUBES-PHI: Detection(0.965), Classification relaxed (0.95), Classification strict (0.937)
Medoccan: Detection(0.972), Classification (0.967) | [
262,
264
] |
What are the other algorithms tested? | NER model, CRF classifier trained with sklearn-crfsuite, classifier has been developed that consists of regular-expressions and dictionary look-up | [
250,
252
] |
Does BERT reach the best performance among all the algorithms compared? | No | [
258,
264
] |
What are the clinical datasets used in the paper? | MEDDOCAN, NUBes-PHI | [
246,
247,
248
] |
how is model compactness measured? | Using file size on disk | [
286
] |
what was the baseline? | system presented by deri2016grapheme | [
282
] |
what evaluation metrics were used? | Phoneme Error Rate (PER), Word Error Rate (WER), Word Error Rate 100 (WER 100) | [
281
] |
what datasets did they use? | the Carnegie Mellon Pronouncing Dictionary BIBREF12, the multilingual pronunciation corpus collected by deri2016grapheme , ranscriptions extracted from Wiktionary | [
278,
279,
280
] |
Who were the human evaluators used? | 20 evaluators were recruited from our institution and asked to each perform 20 annotations | [
316,
317
] |
Is the template-based model realistic? | Yes | [
304,
313,
314
] |
Is the student reflection data very different from the newspaper data? | Yes | [
292
] |
What is the recent abstractive summarization method in this paper? | pointer networks with coverage mechanism (PG-net) | [
299
] |
Why are prior knowledge distillation techniques models are ineffective in producing student models with vocabularies different from the original teacher models? | While there has been existing work on reducing NLP model vocabulary sizes BIBREF15, distillation techniques cannot utilize these, since they require the student and teacher models to share the same vocabulary and output space. This profoundly limits their potential to further reduce model sizes. | [
324
] |
What state-of-the-art compression techniques were used in the comparison? | baseline without knowledge distillation (termed NoKD), Patient Knowledge Distillation (PKD) | [
338
] |
What is the size of the dataset? | 30,000 | [
347,
350
] |
Which methods are considered to find examples of biases and unwarranted inferences?? | spot patterns by just looking at a collection of images, tag all descriptions with part-of-speech information, I applied Louvain clustering | [
359,
360
] |
What biases are found in the dataset? | Ethnic bias | [
356,
358
] |
What discourse relations does it work best/worst for? | explicit discourse relations | [
388,
389
] |
How much does this model improve state-of-the-art? | the basic model yields good performance for recognizing explicit discourse relations as well, which is comparable with previous best result (92.05% macro F1-score and 93.09% accuracy as reported in BIBREF11 )., full paragraph-level neural network model achieves the best macro-average F1-score of 48.82% in predicting im... | [
388,
389
] |
Where is a question generation model used? | The question generation model provides each candidate answer with a score by measuring semantic relevance between the question and the generated question based on the semantics of the candidate answer. | [
397
] |
Were any of these tasks evaluated in any previous work? | Yes | [
424,
432
] |
Do they build a model to automatically detect demographic, lingustic or psycological dimensons of people? | No | [
441
] |
Which demographic dimensions of people do they obtain? | occupation, industry, profile information, language use, gender | [
436,
437,
439
] |
How do they obtain psychological dimensions of people? | using the Meaning Extraction Method | [
442
] |
What is the baseline? | QA PGNet, Multi-decoder QA PGNet with lookup table embedding | [
463
] |
Is the data de-identified? | Yes | [
454
] |
What embeddings are used? | simple lookup table embeddings learned from scratch, using high-performance contextual embeddings, which are ELMo BIBREF11, BERT BIBREF16 and ClinicalBERT BIBREF13 | [
464
] |
What datasets did they use for evaluation? | CSAT dataset, 20 newsgroups, Fisher Phase 1 corpus | [
489
] |
On top of BERT does the RNN layer work better or the transformer layer? | Transformer over BERT (ToBERT) | [
483,
498,
499
] |
How was this data collected? | CrowdFlower | [
506
] |
What is the average length of dialog? | 4.49 turns | [
509
] |
How are models evaluated in this human-machine communication game? | by training an autocomplete system on 500K randomly sampled sentences from Yelp reviews | [
523
] |
How many participants were trying this communication game? | 100 | [
526
] |
What user variations have been tested? | completion times and accuracies | [
526
] |
What are the baselines used? | Unif and Stopword | [
525
] |
How does the IPA label data after interacting with users? | It defined a sequence labeling task to extract custom entities from user input and label the next action (out of 13 custom actions defined). | [
557,
558
] |
What kind of repetitive and time-consuming activities does their assistant handle? | What kind of topic (or sub-topic) a student has a problem with, At which examination mode (i.e., quiz, chapter level training or exercise, section level training or exercise, or final examination) the student is working right now, the exact question number and exact problem formulation | [
534
] |
How was the audio data gathered? | Through the All India Radio new channel where actors read news. | [
577,
585
] |
What is the GhostVLAD approach? | extension of the NetVLAD, adds Ghost clusters along with the NetVLAD clusters | [
582,
583
] |
What datasets do they evaluate on? | Wall Street Journal (WSJ) portion of the Penn Treebank | [
610
] |
Do they evaluate only on English datasets? | Yes | [
610
] |
What is the invertibility condition? | The neural projector must be invertible. | [
603
] |
Which neural architecture do they use as a base for their attention conflict mechanisms? | GRU-based encoder, interaction block, and classifier consisting of stacked fully-connected layers. | [
637
] |
On which tasks do they test their conflict method? | Task 1: Quora Duplicate Question Pair Detection, Task 2: Ranking questions | [
637,
639
] |
What are the sources of the datasets? | rupnik2016news | [
661
] |
What in-domain text did they use? | PubMed+PMC | [
677
] |
Which languages do they test on for the under-resourced scenario? | English, German | [
713
] |
Are the automatically constructed datasets subject to quality control? | No | [
732,
733,
734,
735,
736,
737,
738,
739,
740,
741,
742,
743,
744,
745
] |
After how many hops does accuracy decrease? | 1-hop links to 2-hops | [
729
] |
How do they control for annotation artificats? | we use several of the MCQA baseline models first introduced in BIBREF0 | [
748,
749
] |
Is WordNet useful for taxonomic reasoning for this task? | Yes | [
727
] |
How do they perform multilingual training? | Multilingual training is performed by randomly alternating between languages for every new minibatch | [
779
] |
Does the model have attention? | Yes | [
776
] |
What architecture does the decoder have? | LSTM | [
776
] |
What architecture does the encoder have? | LSTM | [
776
] |
Do they use attention? | Yes | [
793
] |
What other models do they compare to? | SAN Baseline, BNA, DocQA, R.M-Reader, R.M-Reader+Verifier and DocQA+ELMo | [
798,
799
] |
What is the architecture of the span detector? | adopt a multi-turn answer module for the span detector BIBREF1 | [
794
] |
What evaluation metric do they use? | Accuracy | [
816
] |
What are the baselines? | a score of 40 | [
823,
824
] |
What are the two new strategies? | a method that detects bottlenecks in text-games using the overall reward gained and the knowledge graph state, to leverage knowledge graphs to improve existing exploration algorithms for dealing with combinatorial action-space | [
825
] |
How much better than the baseline is LiLi? | In case of Freebase knowledge base, LiLi model had better F1 score than the single model by 0.20 , 0.01, 0.159 for kwn, unk, and all test Rel type. The values for WordNet are 0.25, 0.1, 0.2.
| [
878,
879,
880,
881,
882
] |
What baseline is used in the experiments? | versions of LiLi | [
878,
879
] |
In what way does LiLi imitate how humans acquire knowledge and perform inference during an interactive conversation? | newly acquired facts are retained in the KB and used in inference for future queries, and that the accumulated knowledge in addition to the updated KB including past inference performances are leveraged to guide future interaction and learning | [
844
] |
What metrics are used to establish that this makes chatbots more knowledgeable and better at learning and conversation? | Coverage, Avg. MCC and avg. +ve F1 score | [
879
] |
What are the components of the general knowledge learning engine? | Answer with content missing: (list)
LiLi should have the following capabilities:
1. to formulate an inference strategy for a given query that embeds processing and interactive actions.
2. to learn interaction behaviors (deciding what to ask and when to ask the user).
3. to leverage the acquired knowledge in the current... | [
844
] |
What is the architecture of the model? | one-layer CNN structure from previous works BIBREF22 , BIBREF4 | [
895,
902
] |
What are the baseline methods? | (1) Naive, (2) mSDA BIBREF7, (3) NaiveNN, (4) AuxNN BIBREF4, (5) ADAN BIBREF16, (6) MMD | [
909,
910,
911
] |
What are the source and target domains? | Book, electronics, beauty, music, IMDB, Yelp, cell phone, baby, DVDs, kitchen | [
920
] |
How do they deal with unknown distribution senses? | The Näive-Bayes classifier is corrected so it is not biased to most frequent classes | [
926,
932
] |
Do they report results only on English data? | Yes | [
957,
958,
961
] |
What conclusions do the authors draw from their finding that the emotional appeal of ISIS and Catholic materials are similar? | both corpuses used words that aim to inspire readers while avoiding fear, actual words that lead to these effects are very different in the two contexts, our findings indicate that, using proper methods, automated analysis of large bodies of textual data can provide novel insight insight into extremist propaganda | [
976
] |
How are similarities and differences between the texts from violent and non-violent religious groups analyzed? | By using topic modeling and unsupervised emotion detection on ISIS materials and articles from Catholic women forum | [
952,
953
] |
How are prominent topics idenified in Dabiq and Rumiyah? | LDA, non-negative matrix factorization (NMF) | [
967,
968
] |
Are the images from a specific domain? | Yes | [
981
] |
Which datasets are used? | Existential (OneShape, MultiShapes), Spacial (TwoShapes, Multishapes), Quantification (Count, Ratio) datasets are generated from ShapeWorldICE | [
989
] |
Which existing models are evaluated? | Show&Tell and LRCN1u | [
991
] |
How is diversity measured? | diversity score as the ratio of observed number versus optimal number | [
988
] |
What state-of-the-art deep neural network is used? | LSTM model | [
1006
] |
What boundary assembling method is used? | This motivates us to carry out a backward greedy search over each sentence's label sequence to identify word boundaries. If two words segmented in a sentence are identified as nouns, and one word is immediately before the other, we assemble their boundaries, creating a new word candidate for entity recognition. | [
1009
] |
What is the model performance on target language reading comprehension? | Table TABREF6, Table TABREF8 | [
1021,
1022
] |
What model is used as a baseline? | pre-trained multi-BERT | [
1019
] |
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