Unnamed: 0 int64 0 886 | question stringlengths 19 151 | answer stringlengths 1 1.08k | abstract stringlengths 279 2.02k | introduction stringlengths 52 9.04k ⌀ |
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590 | What are the two PharmaCoNER subtasks? | Entity identification with offset mapping and concept indexing | Analogies such as man is to king as woman is to X are often used to illustrate the amazing power of word embeddings. Concurrently, they have also exposed how strongly human biases are encoded in vector spaces built on natural language. While finding that queen is the answer to man is to king as woman is to X leaves us ... | Word embeddings are distributed representations of texts which capture similarities between words. Beside improving a wide variety of NLP tasks, the power of word embeddings is often also tested intrinsically. Together with the idea of training word embeddings, BIBREF0 introduced the idea of testing the soundness of em... |
591 | How do they perform data augmentation? | They randomly sample sentences from Wikipedia that contains an object RC and add them to training data | Named entity recognition (NER) is the very first step in the linguistic processing of any new domain. It is currently a common process in BioNLP on English clinical text. However, it is still in its infancy in other major languages, as it is the case for Spanish. Presented under the umbrella of the PharmaCoNER shared t... | Named Entity Recognition (ner) is considered a necessary first step in the linguistic processing of any new domain, as it facilitates the development of applications showing co-occurrences of domain entities, cause-effect relations among them, and, eventually, it opens the (still to be reached) possibility of understan... |
592 | What are the characteristics of the rural dialect? | It uses particular forms of a concept rather than all of them uniformly | We explore the utilities of explicit negative examples in training neural language models. Negative examples here are incorrect words in a sentence, such as"barks"in"*The dogs barks". Neural language models are commonly trained only on positive examples, a set of sentences in the training data, but recent studies sugge... | intro Despite not being exposed to explicit syntactic supervision, neural language models (LMs), such as recurrent neural networks, are able to generate fluent and natural sentences, suggesting that they induce syntactic knowledge about the language to some extent. However, it is still under debate whether such induced... |
594 | What is the performance of the models on the tasks? | Overall accuracy per model is: 5-gram (60.5), LSTM (68.9), TXL (68.7), GPT-2 (80.1) | This paper explores a simple and efficient baseline for text classification. Our experiments show that our fast text classifier fastText is often on par with deep learning classifiers in terms of accuracy, and many orders of magnitude faster for training and evaluation. We can train fastText on more than one billion wo... | Text classification is an important task in Natural Language Processing with many applications, such as web search, information retrieval, ranking and document classification BIBREF0 , BIBREF1 . Recently, models based on neural networks have become increasingly popular BIBREF2 , BIBREF3 , BIBREF4 . While these models a... |
597 | What other non-neural baselines do the authors compare to? | bag of words, tf-idf, bag-of-means | The dependency of the generalization error of neural networks on model and dataset size is of critical importance both in practice and for understanding the theory of neural networks. Nevertheless, the functional form of this dependency remains elusive. In this work, we present a functional form which approximates well... | With the success and heightened adoption of neural networks for real world tasks, some questions remain poorly answered. For a given task and model architecture, how much data would one require to reach a prescribed performance level? How big a model would be needed? Addressing such questions is made especially diffic... |
599 | On what dataset is Aristo system trained? | Aristo Corpus
Regents 4th
Regents 8th
Regents `12th
ARC-Easy
ARC-challenge | In this work, we present a simple yet better variant of Self-Critical Sequence Training. We make a simple change in the choice of baseline function in REINFORCE algorithm. The new baseline can bring better performance with no extra cost, compared to the greedy decoding baseline. | Self-Critical Sequence Training(SCST), upon its release, has been a popular way to train sequence generation models. While originally proposed for image captioning task, SCST not only has become the new standard for training captioning models BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBRE... |
605 | What language technologies have been introduced in the past? | - Font & Keyboard
- Speech-to-Text
- Text-to-Speech
- Text Prediction
- Spell Checker
- Grammar Checker
- Text Search
- Machine Translation
- Voice to Text Search
- Voice to Speech Search | Understanding the semantic relationships between terms is a fundamental task in natural language processing applications. While structured resources that can express those relationships in a formal way, such as ontologies, are still scarce, a large number of linguistic resources gathering dictionary definitions is beco... | This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: http://creativecommons.org/licenses/by/4.0/ Many natural language understanding tasks such as Text Entailment and Question Answering systems are dependent on the interpretation of the semantic relationships between te... |
608 | How do they define local variance? | The reciprocal of the variance of the attention distribution | In this paper, we investigate whether text from a Community Question Answering (QA) platform can be used to predict and describe real-world attributes. We experiment with predicting a wide range of 62 demographic attributes for neighbourhoods of London. We use the text from QA platform of Yahoo! Answers and compare our... | Recent years have seen a huge boom in the number of different social media platforms available to users. People are increasingly using these platforms to voice their opinions or let others know about their whereabouts and activities. Each of these platforms has its own characteristics and is used for different purposes... |
610 | how do they measure discussion quality? | Measuring three aspects: argumentation, specificity and knowledge domain. | Contextual word embeddings (e.g. GPT, BERT, ELMo, etc.) have demonstrated state-of-the-art performance on various NLP tasks. Recent work with the multilingual version of BERT has shown that the model performs very well in zero-shot and zero-resource cross-lingual settings, where only labeled English data is used to fin... | Contextual word embeddings BIBREF0 , BIBREF1 , BIBREF2 have been successfully applied to various NLP tasks, including named entity recognition, document classification, and textual entailment. The multilingual version of BERT (which is trained on Wikipedia articles from 100 languages and equipped with a 110,000 shared ... |
611 | what were the baselines? | 2008 Punyakanok et al.
2009 Zhao et al. + ME
2008 Toutanova et al.
2010 Bjorkelund et al.
2015 FitzGerald et al.
2015 Zhou and Xu
2016 Roth and Lapata
2017 He et al.
2017 Marcheggiani et al.
2017 Marcheggiani and Titov
2018 Tan et al.
2018 He et al.
2018 Strubell et al.
2018 Cai et al.
2018 He et al.
201... | Classroom discussions in English Language Arts have a positive effect on students' reading, writing and reasoning skills. Although prior work has largely focused on teacher talk and student-teacher interactions, we focus on three theoretically-motivated aspects of high-quality student talk: argumentation, specificity, ... | Current research, theory, and policy surrounding K-12 instruction in the United States highlight the role of student-centered disciplinary discussions (i.e. discussions related to a specific academic discipline or school subject such as physics or English Language Arts) in instructional quality and student learning opp... |
612 | Which soft-selection approaches are evaluated? | LSTM and BERT | Semantic role labeling (SRL) aims to discover the predicateargument structure of a sentence. End-to-end SRL without syntactic input has received great attention. However, most of them focus on either span-based or dependency-based semantic representation form and only show specific model optimization respectively. Mean... | The purpose of semantic role labeling (SRL) is to derive the meaning representation for a sentence, which is beneficial to a wide range of natural language processing (NLP) tasks BIBREF0 , BIBREF1 . SRL can be formed as four subtasks, including predicate detection, predicate disambiguation, argument identification and ... |
616 | How big is slot filing dataset? | Dataset has 1737 train, 497 dev and 559 test sentences. | Recently, neural models pretrained on a language modeling task, such as ELMo (Peters et al., 2017), OpenAI GPT (Radford et al., 2018), and BERT (Devlin et al., 2018), have achieved impressive results on various natural language processing tasks such as question-answering and natural language inference. In this paper, w... | We have seen rapid progress in machine reading compression in recent years with the introduction of large-scale datasets, such as SQuAD BIBREF3 , MS MARCO BIBREF4 , SearchQA BIBREF5 , TriviaQA BIBREF6 , and QUASAR-T BIBREF7 , and the broad adoption of neural models, such as BiDAF BIBREF8 , DrQA BIBREF9 , DocumentQA BIB... |
617 | How large is the dataset they generate? | 4.756 million sentences | Slot Filling is the task of extracting the semantic concept from a given natural language utterance. Recently it has been shown that using contextual information, either in work representations (e.g., BERT embedding) or in the computation graph of the model, could improve the performance of the model. However, recent w... | Slot Filling (SF) is the task of identifying the semantic concept expressed in natural language utterance. For instance, consider a request to edit an image expressed in natural language: “Remove the blue ball on the table and change the color of the wall to brown”. Here, the user asks for an "Action" (i.e., removing) ... |
625 | What are the weaknesses of their proposed interpretability quantification method? | can be biased by dataset used and may generate categories which are suboptimal compared to human designed categories | This paper is to explore the possibility to use alternative data and artificial intelligence techniques to trade stocks. The efficacy of the daily Twitter sentiment on predicting the stock return is examined using machine learning methods. Reinforcement learning(Q-learning) is applied to generate the optimal trading po... | In a world where traditional financial information is ubiquitous and the financial models are largely homogeneous, finding hidden information that has not been priced in from alternative data is critical. The recent development in Natural Language Processing provides such opportunities to look into text data in additio... |
627 | How was lexical diversity measured? | By computing number of unique responses and number of responses divided by the number of unique responses to that question for each of the questions | Designing a reliable natural language (NL) interface for querying tables has been a longtime goal of researchers in both the data management and natural language processing (NLP) communities. Such an interface receives as input an NL question, translates it into a formal query, executes the query and returns the result... | Natural language interfaces have been gaining significant popularity, enabling ordinary users to write and execute complex queries. One of the prominent paradigms for developing NL interfaces is semantic parsing, which is the mapping of NL phrases into a formal language. As Machine Learning techniques are standardly us... |
628 | What human evaluation method is proposed? | comparing the summary with the text instead of the reference and labeling the candidate bad if it is incorrect or irrelevant | Creative tasks such as ideation or question proposal are powerful applications of crowdsourcing, yet the quantity of workers available for addressing practical problems is often insufficient. To enable scalable crowdsourcing thus requires gaining all possible efficiency and information from available workers. One optio... | Crowdsourcing applications vary from basic, self-contained tasks such as image recognition or labeling BIBREF0 all the way to open-ended and creative endeavors such as collaborative writing, creative question proposal, or more general ideation BIBREF1 . Yet scaling the crowd to very large sets of creative tasks may req... |
631 | What languages are represented in the dataset? | EN, JA, ES, AR, PT, KO, TH, FR, TR, RU, IT, DE, PL, NL, EL, SV, FA, VI, FI, CS, UK, HI, DA, HU, NO, RO, SR, LV, BG, UR, TA, MR, BN, IN, KN, ET, SL, GU, CY, ZH, CKB, IS, LT, ML, SI, IW, NE, KM, MY, TL, KA, BO | Short-text classification, like all data science, struggles to achieve high performance using limited data. As a solution, a short sentence may be expanded with new and relevant feature words to form an artificially enlarged dataset, and add new features to testing data. This paper applies a novel approach to text expa... | The web has provided researchers with vast amounts of unlabeled text data, and enabled the development of increasingly sophisticated language models which can achieve state of the art performance despite having no task specific training BIBREF0, BIBREF1, BIBREF2. It is desirable to adapt these models for bespoke tasks ... |
633 | How faster is training and decoding compared to former models? | Proposed vs best baseline:
Decoding: 8541 vs 8532 tokens/sec
Training: 8h vs 8h | One of the basic tasks of computational language documentation (CLD) is to identify word boundaries in an unsegmented phonemic stream. While several unsupervised monolingual word segmentation algorithms exist in the literature, they are challenged in real-world CLD settings by the small amount of available data. A poss... | All over the world, languages are disappearing at an unprecedented rate, fostering the need for specific tools aimed to aid field linguists to collect, transcribe, analyze, and annotate endangered language data (e.g. BIBREF0, BIBREF1). A remarkable effort in this direction has improved the data collection procedures an... |
637 | What datasets are used to evaluate the model? | WN18 and FB15k | We propose a methodology that adapts graph embedding techniques (DeepWalk (Perozzi et al., 2014) and node2vec (Grover and Leskovec, 2016)) as well as cross-lingual vector space mapping approaches (Least Squares and Canonical Correlation Analysis) in order to merge the corpus and ontological sources of lexical knowledge... | The prominent model for representing semantics of words is the distributional vector space model BIBREF2 and the prevalent approach for constructing these models is the distributional one which assumes that semantics of a word can be predicted from its context, hence placing words with similar contexts in close proximi... |
638 | What is the source of the dataset? | Online sites tagged as fake news site by Verafiles and NUJP and news website in the Philippines, including Pilipino Star Ngayon, Abante, and Bandera | Recent studies on knowledge base completion, the task of recovering missing facts based on observed facts, demonstrate the importance of learning embeddings from multi-step relations. Due to the size of knowledge bases, previous works manually design relation paths of observed triplets in symbolic space (e.g. random wa... | null |
640 | What metadata is included? | besides claim, label and claim url, it also includes a claim ID, reason, category, speaker, checker, tags, claim entities, article title, publish data and claim date | In this paper, we propose a novel unsupervised learning method for the lexical acquisition of words related to places visited by robots, from human continuous speech signals. We address the problem of learning novel words by a robot that has no prior knowledge of these words except for a primitive acoustic model. Furth... | Autonomous robots, such as service robots, operating in the human living environment with humans have to be able to perform various tasks and language communication. To this end, robots are required to acquire novel concepts and vocabulary on the basis of the information obtained from their sensors, e.g., laser sensors... |
641 | How much important is the visual grounding in the learning of the multilingual representations? | performance is significantly degraded without pixel data | We contribute the largest publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification. It is collected from 26 fact checking websites in English, paired with textual sources and rich metadata, and labelled for veracity by human expert journalists. We present an in-de... | Misinformation and disinformation are two of the most pertinent and difficult challenges of the information age, exacerbated by the popularity of social media. In an effort to counter this, a significant amount of manual labour has been invested in fact checking claims, often collecting the results of these manual chec... |
642 | How is the generative model evaluated? | Comparing BLEU score of model with and without attention | There has been significant interest recently in learning multilingual word embeddings -- in which semantically similar words across languages have similar embeddings. State-of-the-art approaches have relied on expensive labeled data, which is unavailable for low-resource languages, or have involved post-hoc unification... | Recent advances in learning distributed representations for words (i.e., word embeddings) have resulted in improvements across numerous natural language understanding tasks BIBREF0 , BIBREF1 . These methods use unlabeled text corpora to model the semantic content of words using their co-occurring context words. Key to ... |
643 | What is an example of a health-related tweet? | The health benefits of alcohol consumption are more limited than previously thought, researchers say | The ability to reason with natural language is a fundamental prerequisite for many NLP tasks such as information extraction, machine translation and question answering. To quantify this ability, systems are commonly tested whether they can recognize textual entailment, i.e., whether one sentence can be inferred from an... | The ability to determine entailment or contradiction between natural language text is essential for improving the performance in a wide range of natural language processing tasks. Recognizing Textual Entailment (RTE) is a task primarily designed to determine whether two natural language sentences are independent, contr... |
645 | What is the challenge for other language except English | not researched as much as English | The cryptocurrency is attracting more and more attention because of the blockchain technology. Ethereum is gaining a significant popularity in blockchain community, mainly due to the fact that it is designed in a way that enables developers to write smart contracts and decentralized applications (Dapps). There are many... | Since Satoshi Nakamoto published the article "Bitcoin: A Peer-to-Peer Electronic Cash System" in 2008 BIBREF0 , and after the official launch of Bitcoin in 2009, technologies such as blockchain and cryptocurrency have attracted attention from academia and industry. At present, the technologies have been applied to many... |
647 | Which matching features do they employ? | Matching features from matching sentences from various perspectives. | This paper studies how the linguistic components of blogposts collected from Sina Weibo, a Chinese microblogging platform, might affect the blogposts' likelihood of being censored. Our results go along with King et al. (2013)'s Collective Action Potential (CAP) theory, which states that a blogpost's potential of causin... | In 2019, Freedom in the World, a yearly survey produced by Freedom House that measures the degree of civil liberties and political rights in every nation, recorded the 13th consecutive year of decline in global freedom. This decline spans across long-standing democracies such as USA as well as authoritarian regimes suc... |
651 | How large is the corpus they use? | 449050 | Every day media generate large amounts of text. An unbiased view on media reports requires an understanding of the political bias of media content. Assistive technology for estimating the political bias of texts can be helpful in this context. This study proposes a simple statistical learning approach to predict politi... | Modern media generate a large amount of content at an ever increasing rate. Keeping an unbiased view on what media report on requires to understand the political bias of texts. In many cases it is obvious which political bias an author has. In other cases some expertise is required to judge the political bias of a text... |
653 | How many shared layers are in the system? | 1 | Methods for unsupervised hypernym detection may broadly be categorized according to two paradigms: pattern-based and distributional methods. In this paper, we study the performance of both approaches on several hypernymy tasks and find that simple pattern-based methods consistently outperform distributional methods on ... | Hierarchical relationships play a central role in knowledge representation and reasoning. Hypernym detection, i.e., the modeling of word-level hierarchies, has long been an important task in natural language processing. Starting with BIBREF0 , pattern-based methods have been one of the most influential approaches to th... |
656 | How many layers of self-attention does the model have? | 1, 4, 8, 16, 32, 64 | It has recently been shown that word embeddings encode social biases, with a harmful impact on downstream tasks. However, to this point there has been no similar work done in the field of graph embeddings. We present the first study on social bias in knowledge graph embeddings, and propose a new metric suitable for mea... | Recent work in the word embeddings literature has shown that embeddings encode gender and racial biases, BIBREF0, BIBREF1, BIBREF2. These biases can have harmful effects in downstream tasks including coreference resolution, BIBREF3 and machine translation, BIBREF4, leading to the development of a range of methods to tr... |
665 | what are the state of the art methods? | S2VT, RGB (VGG), RGB (VGG)+Flow (AlexNet), LSTM-E (VGG), LSTM-E (C3D) and Yao et al. | Speaker Recognition is a challenging task with essential applications such as authentication, automation, and security. The SincNet is a new deep learning based model which has produced promising results to tackle the mentioned task. To train deep learning systems, the loss function is essential to the network performa... | Speaker Recognition is an essential task with applications in biometric authentication, identification, and security among others BIBREF0 . The field is divided into two main subtasks: Speaker Identification and Speaker Verification. In Speaker Identification, given an audio sample, the model tries to identify to which... |
668 | Which four languages do they experiment with? | German, English, Italian, Chinese | De-identification is the process of removing 18 protected health information (PHI) from clinical notes in order for the text to be considered not individually identifiable. Recent advances in natural language processing (NLP) has allowed for the use of deep learning techniques for the task of de-identification. In this... | [block] 1 5mm * 2pt*22pt [block] 2.1 5mm [block] 2.1.1 5mm |
669 | Does DCA or GMM-based attention perform better in experiments? | About the same performance | The majority of existing speech emotion recognition models are trained and evaluated on a single corpus and a single language setting. These systems do not perform as well when applied in a cross-corpus and cross-language scenario. This paper presents results for speech emotion recognition for 4 languages in both singl... | Speech conveys human emotions most naturally. In recent years there has been an increased research interest in speech emotion recognition domain. The first step in a typical SER system is extracting linguistic and acoustic features from speech signal. Some para-linguistic studies find Low-Level Descriptor (LLD) feature... |
670 | What evaluation metric is used? | F1 and Weighted-F1 | Despite the ability to produce human-level speech for in-domain text, attention-based end-to-end text-to-speech (TTS) systems suffer from text alignment failures that increase in frequency for out-of-domain text. We show that these failures can be addressed using simple location-relative attention mechanisms that do aw... | Sequence-to-sequence models that use an attention mechanism to align the input and output sequences BIBREF0, BIBREF1 are currently the predominant paradigm in end-to-end TTS. Approaches based on the seminal Tacotron system BIBREF2 have demonstrated naturalness that rivals that of human speech for certain domains BIBREF... |
671 | Is any data-to-text generation model trained on this new corpus, what are the results? | Yes, Transformer based seq2seq is evaluated with average BLEU 0.519, METEOR 0.388, ROUGE 0.631 CIDEr 2.531 and SER 2.55%. | The detection of offensive language in the context of a dialogue has become an increasingly important application of natural language processing. The detection of trolls in public forums (Galan-Garcia et al., 2016), and the deployment of chatbots in the public domain (Wolf et al., 2017) are two examples that show the n... | The detection of offensive language has become an important topic as the online community has grown, as so too have the number of bad actors BIBREF2. Such behavior includes, but is not limited to, trolling in public discussion forums BIBREF3 and via social media BIBREF4, BIBREF5, employing hate speech that expresses pr... |
676 | How are the potentially relevant text fragments identified? | Generate a query out of the claim and querying a search engine, rank the words by means of TF-IDF, use IBM's AlchemyAPI to identify named entities, generate queries of 5–10 tokens, which execute against a search engine, and collect the snippets and the URLs in the results, skipping any result that points to a domain t... | In this paper, we demonstrate the importance of coreference resolution for natural language processing on the example of the TAC Slot Filling shared task. We illustrate the strengths and weaknesses of automatic coreference resolution systems and provide experimental results to show that they improve performance in the ... | Coreference resolution systems group noun phrases (mentions) that refer to the same entity into the same chain. Mentions can be full names (e.g., John Miller), pronouns (e.g., he), demonstratives (e.g., this), comparatives (e.g., the first) or descriptions of the entity (e.g. the 40-year-old) BIBREF0 . Although corefer... |
679 | What dataset did they use? | weibo-100k, Ontonotes, LCQMC and XNLI | The strength of pragmatic inferences systematically depends on linguistic and contextual cues. For example, the presence of a partitive construction increases the strength of a so-called scalar inference: humans perceive the inference that Chris did not eat all of the cookies to be stronger after hearing "Chris ate som... | An important property of human communication is that listeners can infer information beyond the literal meaning of an utterance. One well-studied type of inference is scalar inference BIBREF0, BIBREF1, whereby a listener who hears an utterance with a scalar item like some infers the negation of a stronger alternative w... |
680 | What is the size of the dataset? | 3029 | Most Chinese pre-trained encoders take a character as a basic unit and learn representations according to character's external contexts, ignoring the semantics expressed in the word, which is the smallest meaningful unit in Chinese. Hence, we propose a novel word aligned attention to incorporate word segmentation infor... | Pre-trained language Models (PLM) such as ELMo BIBREF0, BERT BIBREF1, ERNIE BIBREF2 and XLNet BIBREF3 have been proven to capture rich language information from text and then benefit many NLP applications by simple fine-tuning, including sentiment classification, natural language inference, named entity recognition and... |
681 | What are the 12 AV approaches which are examined? | MOCC, OCCAV, COAV, AVeer, GLAD, DistAV, Unmasking, Caravel, GenIM, ImpGI, SPATIUM and NNCD | This work investigates the task-oriented dialogue problem in mixed-domain settings. We study the effect of alternating between different domains in sequences of dialogue turns using two related state-of-the-art dialogue systems. We first show that a specialized state tracking component in multiple domains plays an impo... | In this work, we investigate the problem of task-oriented dialogue in mixed-domain settings. Our work is related to two lines of research in Spoken Dialogue System (SDS), namely task-oriented dialogue system and multi-domain dialogue system. We briefly review the recent literature related to these topics as follows. Ta... |
682 | how was annotation done? | Annotation was done with the help of annotators from Amazon Mechanical Turk on snippets of conversations | Authorship verification (AV) is a research subject in the field of digital text forensics that concerns itself with the question, whether two documents have been written by the same person. During the past two decades, an increasing number of proposed AV approaches can be observed. However, a closer look at the respect... | Digital text forensics aims at examining the originality and credibility of information in electronic documents and, in this regard, at extracting and analyzing information about the authors of the respective texts BIBREF0 . Among the most important tasks of this field are authorship attribution (AA) and authorship ver... |
684 | How do they measure correlation between the prediction and explanation quality? | They look at the performance accuracy of explanation and the prediction performance | The Coronavirus pandemic has taken the world by storm as also the social media. As the awareness about the ailment increased, so did messages, videos and posts acknowledging its presence. The social networking site, Twitter, demonstrated similar effect with the number of posts related to coronavirus showing an unpreced... | Catastrophic global circumstances have a pronounced effect on the lives of human beings across the world. The ramifications of such a scenario are experienced in diverse and multiplicative ways spanning routine tasks, media and news reports, detrimental physical and mental health, and also routine conversations. A simi... |
686 | What datasets are used to evaluate the introduced method? | They used a dataset from Taobao which contained a collection of conversation records between customers and customer service staffs. It contains over five kinds of conversations,
including chit-chat, product and discount consultation, querying delivery progress and after-sales feedback. | Knowledge bases (KBs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform knowledge base completion or link prediction, i.e., predict whether a re... | Before introducing the KB completion task in details, let us return to the classic Word2Vec example of a “royal” relationship between “ $\mathsf {king}$ ” and “ $\mathsf {man}$ ”, and between “ $\mathsf {queen}$ ” and “ $\mathsf {woman}$ .” As illustrated in this example: $v_{king} - v_{man} \approx v_{queen} - v_{woma... |
687 | How do they incorporate human advice? | by converting human advice to first-order logic format and use as an input to calculate gradient | Traditional chatbots usually need a mass of human dialogue data, especially when using supervised machine learning method. Though they can easily deal with single-turn question answering, for multi-turn the performance is usually unsatisfactory. In this paper, we present Lingke, an information retrieval augmented chatb... | $\dagger $ Corresponding author. This paper was partially supported by National Key Research and Development Program of China (No. 2017YFB0304100), National Natural Science Foundation of China (No. 61672343 and No. 61733011), Key Project of National Society Science Foundation of China (No. 15-ZDA041), The Art and Scie... |
688 | What affective-based features are used? | affective features provided by different emotion models such as Emolex, EmoSenticNet, Dictionary of Affect in Language, Affective Norms for English Words and Linguistics Inquiry and Word Count | We consider the task of KBP slot filling -- extracting relation information from newswire documents for knowledge base construction. We present our pipeline, which employs Relational Dependency Networks (RDNs) to learn linguistic patterns for relation extraction. Additionally, we demonstrate how several components such... | The problem of knowledge base population (KBP) – constructing a knowledge base (KB) of facts gleaned from a large corpus of unstructured data – poses several challenges for the NLP community. Commonly, this relation extraction task is decomposed into two subtasks – entity linking, in which entities are linked to alread... |
692 | How big is performance improvement proposed methods are used? | Data augmentation (es) improved Adv es by 20% comparing to baseline
Data augmentation (cs) improved Adv cs by 16.5% comparing to baseline
Data augmentation (cs+es) improved both Adv cs and Adv es by at least 10% comparing to baseline
All models show improvements over adversarial sets
| Latest development of neural models has connected the encoder and decoder through a self-attention mechanism. In particular, Transformer, which is solely based on self-attention, has led to breakthroughs in Natural Language Processing (NLP) tasks. However, the multi-head attention mechanism, as a key component of Trans... | In NLP, Neural language model pre-training has shown to be effective for improving many tasks BIBREF0 , BIBREF1 . Transformer BIBREF2 is based solely on the attention mechanism, and dispensing with recurrent and convolutions entirely. At present, this model has received extensive attentions and plays an key role in man... |
694 | By how much does transfer learning improve performance on this task? | In task 1 best transfer learning strategy improves F1 score by 4.4% and accuracy score by 3.3%, in task 2 best transfer learning strategy improves F1 score by 2.9% and accuracy score by 1.7% | We study few-shot learning in natural language domains. Compared to many existing works that apply either metric-based or optimization-based meta-learning to image domain with low inter-task variance, we consider a more realistic setting, where tasks are diverse. However, it imposes tremendous difficulties to existing ... | Few-shot learning (FSL) BIBREF0 , BIBREF1 , BIBREF2 aims to learn classifiers from few examples per class. Recently, deep learning has been successfully exploited for FSL via learning meta-models from a large number of meta-training tasks. These meta-models can be then used for rapid-adaptation for the target/meta-test... |
695 | How much do they outperform previous state-of-the-art? | On subtask 3 best proposed model has F1 score of 92.18 compared to best previous F1 score of 88.58.
On subtask 4 best proposed model has 85.9, 89.9 and 95.6 compared to best previous results of 82.9, 84.0 and 89.9 on 4-way, 3-way and binary aspect polarity. | We investigate different strategies for automatic offensive language classification on German Twitter data. For this, we employ a sequentially combined BiLSTM-CNN neural network. Based on this model, three transfer learning tasks to improve the classification performance with background knowledge are tested. We compare... | User-generated content in forums, blogs, and social media not only contributes to a deliberative exchange of opinions and ideas but is also contaminated with offensive language such as threats and discrimination against people, swear words or blunt insults. The automatic detection of such content can be a useful suppor... |
698 | How big is the provided treebank? | 1448 sentences more than the dataset from Bhat et al., 2017 | Automated text generation has been applied broadly in many domains such as marketing and robotics, and used to create chatbots, product reviews and write poetry. The ability to synthesize text, however, presents many potential risks, while access to the technology required to build generative models is becoming increas... | The rise of Artificial Intelligence (AI) brings many potential benefits to society, as well as significant risks. These risks take a variety of forms, from autonomous weapons and sophisticated cyber-attacks, to the more subtle techniques of societal manipulation. In particular, the threat this technology poses to maint... |
699 | what dataset was used? | The dataset from a joint ADAPT-Microsoft project | Code-switching is a phenomenon of mixing grammatical structures of two or more languages under varied social constraints. The code-switching data differ so radically from the benchmark corpora used in NLP community that the application of standard technologies to these data degrades their performance sharply. Unlike st... | Code-switching (henceforth CS) is the juxtaposition, within the same speech utterance, of grammatical units such as words, phrases, and clauses belonging to two or more different languages BIBREF0 . The phenomenon is prevalent in multilingual societies where speakers share more than one language and is often prompted b... |
700 | What are the citation intent labels in the datasets? | Background, extends, uses, motivation, compare/contrast, and future work for the ACL-ARC dataset. Background, method, result comparison for the SciCite dataset. | We present ALL-IN-1, a simple model for multilingual text classification that does not require any parallel data. It is based on a traditional Support Vector Machine classifier exploiting multilingual word embeddings and character n-grams. Our model is simple, easily extendable yet very effective, overall ranking 1st (... | Customer feedback analysis is the task of classifying short text messages into a set of predefined labels (e.g., bug, request). It is an important step towards effective customer support. However, a real bottleneck for successful classification of customer feedback in a multilingual environment is the limited transfera... |
703 | How is quality of annotation measured? | Annotators went through various phases to make sure their annotations did not deviate from the mean. | This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-... | The goal of this paper is to improve cross-lingual language understanding (XLU), by carefully studying the effects of training unsupervised cross-lingual representations at a very large scale. We present XLM-R, a transformer-based multilingual masked language model pre-trained on text in 100 languages, which obtains st... |
705 | What accuracy score do they obtain? | the best performing model obtained an accuracy of 0.86 | Knowledge graphs have emerged as an important model for studying complex multi-relational data. This has given rise to the construction of numerous large scale but incomplete knowledge graphs encoding information extracted from various resources. An effective and scalable approach to jointly learn over multiple graphs ... | Reasoning over multi-relational data is a key concept in Artificial Intelligence and knowledge graphs have appeared at the forefront as an effective tool to model such multi-relational data. Knowledge graphs have found increasing importance due to its wider range of important applications such as information retrieval ... |
707 | What percentage of improvement in inference speed is obtained by the proposed method over the newest state-of-the-art methods? | Across 4 datasets, the best performing proposed model (CNN) achieved an average of 363% improvement over the state of the art method (LR-CNN) | Africa has over 2000 languages. Despite this, African languages account for a small portion of available resources and publications in Natural Language Processing (NLP). This is due to multiple factors, including: a lack of focus from government and funding, discoverability, a lack of community, sheer language complexi... | 2144 of all 7111 (30.15%) living languages today are African languages BIBREF1. But only a small portion of linguistic resources for NLP research are built for African languages. As a result, there are only few NLP publications: In all ACL conferences in 2019, only 5 out of 2695 (0.19%) author affiliations were based i... |
708 | What is the metric that is measures in this paper? | error rate in a minimal pair ABX discrimination task | Recently, many works have tried to utilizing word lexicon to augment the performance of Chinese named entity recognition (NER). As a representative work in this line, Lattice-LSTM \cite{zhang2018chinese} has achieved new state-of-the-art performance on several benchmark Chinese NER datasets. However, Lattice-LSTM suffe... | Named Entity Recognition (NER) is concerned with identifying named entities, such as person, location, product, and organization names, in unstructured text. In languages where words are naturally separated (e.g., English), NER was conventionally formulated as a sequence labeling problem, and the state-of-the-art resul... |
710 | Was evaluation metrics and criteria were used to evaluate the output of the cascaded multimodal speech translation? | BLEU scores | Attentional sequence-to-sequence models have become the new standard for machine translation, but one challenge of such models is a significant increase in training and decoding cost compared to phrase-based systems. Here, we focus on efficient decoding, with a goal of achieving accuracy close the state-of-the-art in n... | Attentional sequence-to-sequence models have become the new standard for machine translation over the last two years, and with the unprecedented improvements in translation accuracy comes a new set of technical challenges. One of the biggest challenges is the high training and decoding costs of these neural machine tra... |
711 | What are the two decoding functions? | a linear projection and a bijective function with continuous transformation though ‘affine coupling layer’ of (Dinh et al.,2016). | This paper describes the cascaded multimodal speech translation systems developed by Imperial College London for the IWSLT 2019 evaluation campaign. The architecture consists of an automatic speech recognition (ASR) system followed by a Transformer-based multimodal machine translation (MMT) system. While the ASR compon... | The recently introduced How2 dataset BIBREF2 has stimulated research around multimodal language understanding through the availability of 300h instructional videos, English subtitles and their Portuguese translations. For example, BIBREF3 successfully demonstrates that semantically rich action-based visual features are... |
715 | What are the domains covered in the dataset? | Alarm
Bank
Bus
Calendar
Event
Flight
Home
Hotel
Media
Movie
Music
RentalCar
Restaurant
RideShare
Service
Travel
Weather | Over the past decade, knowledge graphs became popular for capturing structured domain knowledge. Relational learning models enable the prediction of missing links inside knowledge graphs. More specifically, latent distance approaches model the relationships among entities via a distance between latent representations. ... | While machine learning methods conventionally model functions given sample inputs and outputs, a subset of statistical relational learning(SRL) BIBREF0 , BIBREF1 approaches specifically aim to model “things” (entities) and relations between them. These methods usually model human knowledge which is structured in the fo... |
717 | How are the two different models trained? | They pre-train the models using 600000 articles as an unsupervised dataset and then fine-tune the models on small training set. | Neural machine translation (NMT) is a recent and effective technique which led to remarkable improvements in comparison of conventional machine translation techniques. Proposed neural machine translation model developed for the Gujarati language contains encoder-decoder with attention mechanism. In India, almost all th... | India is a highly diverse multilingual country in the world. In India, people of different regions use their own regional speaking languages, which makes India a country having world's second highest number of languages. Human spoken languages in India belongs to several language families. Two main of those families ar... |
718 | Are this techniques used in training multilingual models, on what languages? | English to French and English to German | We investigate the recently developed Bidirectional Encoder Representations from Transformers (BERT) model for the hyperpartisan news detection task. Using a subset of hand-labeled articles from SemEval as a validation set, we test the performance of different parameters for BERT models. We find that accuracy from two ... | SemEval Task 4 BIBREF1 tasked participating teams with identifying news articles that are misleading to their readers, a phenomenon often associated with “fake news” distributed by partisan sources BIBREF2 . We approach the problem through transfer learning to fine-tune a model for the document classification task. We ... |
720 | What metric is used to measure performance? | Accuracy and F1 score for supervised tasks, Pearson's and Spearman's correlation for unsupervised tasks | Network embeddings, which learn low-dimensional representations for each vertex in a large-scale network, have received considerable attention in recent years. For a wide range of applications, vertices in a network are typically accompanied by rich textual information such as user profiles, paper abstracts, etc. We pr... | Networks are ubiquitous, with prominent examples including social networks (e.g., Facebook, Twitter) or citation networks of research papers (e.g., arXiv). When analyzing data from these real-world networks, traditional methods often represent vertices (nodes) as one-hot representations (containing the connectivity inf... |
722 | How do Zipf and Herdan-Heap's laws differ? | Zipf's law describes change of word frequency rate, while Heaps-Herdan describes different word number in large texts (assumed that Hepas-Herdan is consequence of Zipf's) | Recently, bidirectional recurrent network language models (bi-RNNLMs) have been shown to outperform standard, unidirectional, recurrent neural network language models (uni-RNNLMs) on a range of speech recognition tasks. This indicates that future word context information beyond the word history can be useful. However, ... | Language models (LMs) are crucial components in many applications, such as speech recognition and machine translation. The aim of language models is to compute the probability of any given sentence INLINEFORM0 , which can be calculated as DISPLAYFORM0 The task of LMs is to calculate the probability of word INLINEFORM0... |
724 | How are the synthetic examples generated? | Random perturbation of Wikipedia sentences using mask-filling with BERT, backtranslation and randomly drop out | Automatic text summarization is generally considered as a challenging task in the NLP community. One of the challenges is the publicly available and large dataset that is relatively rare and difficult to construct. The problem is even worse for low-resource languages such as Indonesian. In this paper, we present IndoSu... | The goal of text summarization task is to produce a summary from a set of documents. The summary should retain important information and be reasonably shorter than the original documents BIBREF0 . When the set of documents contains only a single document, the task is usually referred to as single-document summarization... |
725 | By how much does the new parser outperform the current state-of-the-art? | Proposed method achieves 94.5 UAS and 92.4 LAS compared to 94.3 and 92.2 of best state-of-the -art greedy based parser. Best state-of-the art parser overall achieves 95.8 UAS and 94.6 LAS. | Text generation has made significant advances in the last few years. Yet, evaluation metrics have lagged behind, as the most popular choices (e.g., BLEU and ROUGE) may correlate poorly with human judgments. We propose BLEURT, a learned evaluation metric based on BERT that can model human judgments with a few thousand p... | In the last few years, research in natural text generation (NLG) has made significant progress, driven largely by the neural encoder-decoder paradigm BIBREF0, BIBREF1 which can tackle a wide array of tasks including translation BIBREF2, summarization BIBREF3, BIBREF4, structured-data-to-text generation BIBREF5, BIBREF6... |
726 | What experimental evaluation is used? | root mean square error between the actual and the predicted price of Bitcoin for every minute | We present a novel transition system, based on the Covington non-projective parser, introducing non-local transitions that can directly create arcs involving nodes to the left of the current focus positions. This avoids the need for long sequences of No-Arc transitions to create long-distance arcs, thus alleviating err... | Greedy transition-based parsers are popular in NLP, as they provide competitive accuracy with high efficiency. They syntactically analyze a sentence by greedily applying transitions, which read it from left to right and produce a dependency tree. However, this greedy process is prone to error propagation: one wrong cho... |
730 | Could you tell me more about the metrics used for performance evaluation? | BLUE utilizes different metrics for each of the tasks: Pearson correlation coefficient, F-1 scores, micro-averaging, and accuracy | In this paper, we describe the system submitted for the shared task on Social Media Mining for Health Applications by the team Light. Previous works demonstrate that LSTMs have achieved remarkable performance in natural language processing tasks. We deploy an ensemble of two LSTM models. The first one is a pretrained l... | In recent years, there has been a rapid growth in the usage of social media. People post their day-to-day happenings on regular basis. BIBREF0 propose four tasks for detecting drug names, classifying medication intake, classifying adverse drug reaction and detecting vaccination behavior from tweets. We participated in ... |
731 | Why does the model improve in monolingual spaces as well? | because word pair similarity increases if the two words translate to similar parts of the cross-lingual embedding space | Inspired by the success of the General Language Understanding Evaluation benchmark, we introduce the Biomedical Language Understanding Evaluation (BLUE) benchmark to facilitate research in the development of pre-training language representations in the biomedicine domain. The benchmark consists of five tasks with ten d... | With the growing amount of biomedical information available in textual form, there have been significant advances in the development of pre-training language representations that can be applied to a range of different tasks in the biomedical domain, such as pre-trained word embeddings, sentence embeddings, and contextu... |
733 | How is annotation projection done when languages have different word order? | Word alignments are generated for parallel text, and aligned words are assumed to also share AMR node alignments. | Natural Language Processing (NLP) systems often make use of machine learning techniques that are unfamiliar to end-users who are interested in analyzing clinical records. Although NLP has been widely used in extracting information from clinical text, current systems generally do not support model revision based on feed... | Electronic Health Records (EHRs) are organized collections of information about individual patients. They are designed such that they can be shared across different settings for providing health care services. The Institute of Medicine committee on improving the patient record has recognized the importance of using EHR... |
734 | What's the precision of the system? | 0.8320 on semantic typing, 0.7194 on entity matching | Abstract Meaning Representation (AMR) annotation efforts have mostly focused on English. In order to train parsers on other languages, we propose a method based on annotation projection, which involves exploiting annotations in a source language and a parallel corpus of the source language and a target language. Using ... | Abstract Meaning Representation (AMR) parsing is the process of converting natural language sentences into their corresponding AMR representations BIBREF0 . An AMR is a graph with nodes representing the concepts of the sentence and edges representing the semantic relations between them. Most available AMR datasets larg... |
736 | Which of the two ensembles yields the best performance? | Answer with content missing: (Table 2) CONCAT ensemble | Many businesses and consumers are extending the capabilities of voice-based services such as Amazon Alexa, Google Home, Microsoft Cortana, and Apple Siri to create custom voice experiences (also known as skills). As the number of these experiences increases, a key problem is the discovery of skills that can be used to ... | Modern speech-based assistants, such as Amazon Alexa, Google Home, Microsoft Cortana, and Apple Siri, enable users to complete daily tasks such as shopping, setting reminders, and playing games using voice commands. Such human-like interfaces create a rich experience for users by enabling them to complete many tasks ha... |
739 | How was a quality control performed so that the text is noisy but the annotations are accurate? | The authors believe that the Wikilinks corpus contains ground truth annotations while being noisy. They discard mentions that cannot have ground-truth verified by comparison with Wikipedia. | The general trend in NLP is towards increasing model capacity and performance via deeper neural networks. However, simply stacking more layers of the popular Transformer architecture for machine translation results in poor convergence and high computational overhead. Our empirical analysis suggests that convergence is ... | The capability of deep neural models of handling complex dependencies has benefited various artificial intelligence tasks, such as image recognition where test error was reduced by scaling VGG nets BIBREF0 up to hundreds of convolutional layers BIBREF1. In NLP, deep self-attention networks have enabled large-scale pret... |
740 | Is it a neural model? How is it trained? | No, it is a probabilistic model trained by finding feature weights through gradient ascent | We address the task of Named Entity Disambiguation (NED) for noisy text. We present WikilinksNED, a large-scale NED dataset of text fragments from the web, which is significantly noisier and more challenging than existing news-based datasets. To capture the limited and noisy local context surrounding each mention, we d... | Named Entity Disambiguation (NED) is the task of linking mentions of entities in text to a given knowledge base, such as Freebase or Wikipedia. NED is a key component in Entity Linking (EL) systems, focusing on the disambiguation task itself, independently from the tasks of Named Entity Recognition (detecting mention b... |
743 | How they evaluate quality of generated output? | Through human evaluation where they are asked to evaluate the generated output on a likert scale. | Image description task has been invariably examined in a static manner with qualitative presumptions held to be universally applicable, regardless of the scope or target of the description. In practice, however, different viewers may pay attention to different aspects of the image, and yield different descriptions or i... | Recent advances in visual language field enabled by deep learning techniques have succeeded in bridging the gap between vision and language in a variety of tasks, ranging from describing the image BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 to answering questions about the image BIBREF4 , BIBREF5 . Such achievements were pos... |
744 | What are the four forums the data comes from? | Darkode, Hack Forums, Blackhat and Nulled. | We propose a novel text generation task, namely Curiosity-driven Question Generation. We start from the observation that the Question Generation task has traditionally been considered as the dual problem of Question Answering, hence tackling the problem of generating a question given the text that contains its answer. ... | The growing interest in Machine Reading Comprehension (MRC) has sparked significant research efforts on Question Generation (QG), the dual task to Question Answering (QA). In QA, the objective is to produce an adequate response given a query and a text; conversely, for QG, the task is generally defined as generating re... |
749 | How are sentence embeddings incorporated into the speech recognition system? | BERT generates sentence embeddings that represent words in context. These sentence embeddings are merged into a single conversational-context vector that is used to calculate a gated embedding and is later combined with the output of the decoder h to provide the gated activations for the next hidden layer. | Encoder-decoder models typically only employ words that are frequently used in the training corpus to reduce the computational costs and exclude noise. However, this vocabulary set may still include words that interfere with learning in encoder-decoder models. This paper proposes a method for selecting more suitable wo... | Encoder-decoder models BIBREF0 are effective in tasks such as machine translation ( BIBREF1 , BIBREF1 ; BIBREF2 , BIBREF2 ) and grammatical error correction BIBREF3 . Vocabulary in encoder-decoder models is generally selected from the training corpus in descending order of frequency, and low-frequency words are replace... |
750 | How different is the dataset size of source and target? | the training dataset is large while the target dataset is usually much smaller | We present a novel conversational-context aware end-to-end speech recognizer based on a gated neural network that incorporates conversational-context/word/speech embeddings. Unlike conventional speech recognition models, our model learns longer conversational-context information that spans across sentences and is conse... | In a long conversation, there exists a tendency of semantically related words, or phrases reoccur across sentences, or there exists topical coherence. Existing speech recognition systems are built at individual, isolated utterance level in order to make building systems computationally feasible. However, this may lose ... |
753 | What type of documents are supported by the annotation platform? | Variety of formats supported (PDF, Word...), user can define content elements of document | Open book question answering is a type of natural language based QA (NLQA) where questions are expected to be answered with respect to a given set of open book facts, and common knowledge about a topic. Recently a challenge involving such QA, OpenBookQA, has been proposed. Unlike most other NLQA tasks that focus on lin... | Natural language based question answering (NLQA) not only involves linguistic understanding, but often involves reasoning with various kinds of knowledge. In recent years, many NLQA datasets and challenges have been proposed, for example, SQuAD BIBREF0 , TriviaQA BIBREF1 and MultiRC BIBREF2 , and each of them have thei... |
756 | What are the strong baselines you have? | optimize single task with no synthetic data | Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forward neural network classification decisions. In the present work, we extend the usage of LRP to recurrent neural networks. We propose a specif... | Semantic composition plays an important role in sentiment analysis of phrases and sentences. This includes detecting the scope and impact of negation in reversing a sentiment's polarity, as well as quantifying the influence of modifiers, such as degree adverbs and intensifiers, in rescaling the sentiment's intensity BI... |
757 | What are causal attribution networks? | networks where nodes represent causes and effects, and directed edges represent cause-effect relationships proposed by humans | Recently advancements in sequence-to-sequence neural network architectures have led to an improved natural language understanding. When building a neural network-based Natural Language Understanding component, one main challenge is to collect enough training data. The generation of a synthetic dataset is an inexpensive... | One of the main challenges in building a Natural Language Understanding (NLU) component for a specific task is the necessary human effort to encode the task's specific knowledge. In traditional NLU components, this was done by creating hand-written rules. In today's state-of-the-art NLU components, significant amounts ... |
760 | how did they ask if a tweet was racist? | if it includes negative utterances, negative generalizations and insults concerning ethnicity, nationality, religion and culture. | This research project aimed to overcome the challenge of analysing human language relationships, facilitate the grouping of languages and formation of genealogical relationship between them by developing automated comparison techniques. Techniques were based on the phonetic representation of certain key words and conce... | The need to uncover presumed underlying linguistic evolutionary principles and analyse correlation between world's languages has entailed this research. For centuries people have been speculating about the origins of language, however this subject is still obscure. Non-automated linguistic analysis of language relation... |
763 | How does the model compute the likelihood of executing to the correction semantic denotation? | By treating logical forms as a latent variable and training a discriminative log-linear model over logical form y given x. | Collaborative robotics requires effective communication between a robot and a human partner. This work proposes a set of interpretive principles for how a robotic arm can use pointing actions to communicate task information to people by extending existing models from the related literature. These principles are evaluat... | Recent years have seen a rapid increase of robotic deployment, beyond traditional applications in cordoned-off workcells in factories, into new, more collaborative use-cases. For example, social robotics and service robotics have targeted scenarios like rehabilitation, where a robot operates in close proximity to a hum... |
766 | What are state of the art methods authors compare their work with? | ISOT dataset: LLVM
Liar dataset: Hybrid CNN and LSTM with attention | Speech recognition and other natural language tasks have long benefited from voting-based algorithms as a method to aggregate outputs from several systems to achieve a higher accuracy than any of the individual systems. Diarization, the task of segmenting an audio stream into speaker-homogeneous and co-indexed regions,... | Speaker diarization is the task of segmenting an audio recording in time, indexing each segment by speaker identity. In the standard version of the task BIBREF0, the goal is not to identify known speakers, but to co-index segments that are attributed to the same speaker; in other words, the task implies finding speaker... |
770 | How much improvement do they get? | Their GTRS approach got an improvement of 3.89% compared to SVM and 27.91% compared to Pawlak. | We examine whether neural natural language processing (NLP) systems reflect historical biases in training data. We define a general benchmark to quantify gender bias in a variety of neural NLP tasks. Our empirical evaluation with state-of-the-art neural coreference resolution and textbook RNN-based language models trai... | Natural language processing (NLP) with neural networks has grown in importance over the last few years. They provide state-of-the-art models for tasks like coreference resolution, language modeling, and machine translation BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 . However, since these models are trained on huma... |
773 | Which languages do they test on? | Answer with content missing: (Applications section) We use Wikipedia articles
in five languages
(Kinyarwanda, Lao, Pashto, Tok Pisin, and a subset of English) as well as the Na dataset of Adams
et al. (2017).
Select:
Kinyarwanda, Lao, Pashto, Tok Pisin, and a subset of English | Natural Language Inference (NLI) datasets often contain hypothesis-only biases---artifacts that allow models to achieve non-trivial performance without learning whether a premise entails a hypothesis. We propose two probabilistic methods to build models that are more robust to such biases and better transfer across dat... | Natural Language Inference (NLI) is often used to gauge a model's ability to understand a relationship between two texts BIBREF0 , BIBREF1 . In NLI, a model is tasked with determining whether a hypothesis (a woman is sleeping) would likely be inferred from a premise (a woman is talking on the phone). The development of... |
774 | What limitations are mentioned? | deciding publisher partisanship, risk annotator bias because of short description text provided to annotators | We propose a simple data augmentation protocol aimed at providing a compositional inductive bias in conditional and unconditional sequence models. Under this protocol, synthetic training examples are constructed by taking real training examples and replacing (possibly discontinuous) fragments with other fragments that ... | This paper proposes a data augmentation protocol for sequence modeling problems. Our approach aims to supply a simple and model-agnostic bias toward compositional reuse of previously observed sequence fragments in novel environments. Consider a language modeling task in which we wish to estimate a probability distribut... |
776 | What are the baselines? | CNN, LSTM, BERT | In this work, we analyze the performance of general deep reinforcement learning algorithms for a task-oriented language grounding problem, where language input contains multiple sub-goals and their order of execution is non-linear. ::: We generate a simple instructional language for the GridWorld environment, that is ... | Task-oriented language grounding refers to the process of extracting semantically meaningful representations of language by mapping it to visual elements and actions in the environment in order to perform the task specified by the instruction BIBREF0. Recent works in this paradigm focus on wide spectrum of natural lang... |
777 | What semantic features help in detecting whether a piece of text is genuine or generated? of | No feature is given, only discussion that semantic features are use in practice and yet to be discovered how to embed that knowledge into statistical decision theory framework. | In this paper, we introduce CAIL2019-SCM, Chinese AI and Law 2019 Similar Case Matching dataset. CAIL2019-SCM contains 8,964 triplets of cases published by the Supreme People's Court of China. CAIL2019-SCM focuses on detecting similar cases, and the participants are required to check which two cases are more similar in... | Similar Case Matching (SCM) plays a major role in legal system, especially in common law legal system. The most similar cases in the past determine the judgment results of cases in common law systems. As a result, legal professionals often spend much time finding and judging similar cases to prove fairness in judgment.... |
778 | Which models do they try out? | DocQA, SAN, QANet, ASReader, LM, Random Guess | Some consider large-scale language models that can generate long and coherent pieces of text as dangerous, since they may be used in misinformation campaigns. Here we formulate large-scale language model output detection as a hypothesis testing problem to classify text as genuine or generated. We show that error expone... | Building on a long history of language generation models that are based on statistical knowledge that people have BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, large-scale, neural network-based language models (LMs) that write paragraph-length text with the coherence of human writing have emerged BIBREF6, BIBRE... |
790 | What are the competing models? | TEACHER FORCING (TF), SCHEDULED SAMPLING (SS), SEQGAN, RANKGAN, LEAKGAN. | Dependency parsing is one of the important natural language processing tasks that assigns syntactic trees to texts. Due to the wider availability of dependency corpora and improved parsing and machine learning techniques, parsing accuracies of supervised learning-based systems have been significantly improved. However,... | [display] 1 0px Semi-Supervised Methods for Out-of-Domain Dependency Parsing Juntao Yu School of Computer Science |
791 | How is the input triple translated to a slot-filling task? | The relation R(x,y) is mapped onto a question q whose answer is y | Exposure bias describes the phenomenon that a language model trained under the teacher forcing schema may perform poorly at the inference stage when its predictions are conditioned on its previous predictions unseen from the training corpus. Recently, several generative adversarial networks (GANs) and reinforcement lea... | Likelihood-based language models with deep neural networks have been widely adopted to tackle language tasks BIBREF0, BIBREF1, BIBREF2, BIBREF3. By far, one of the most popular training strategies is teacher forcing, which derives from the general maximum likelihood estimation (MLE) principle BIBREF4. Under the teacher... |
794 | How is module that analyzes behavioral state trained? | pre-trained to identify the presence of behavior from a sequence of word using the Couples Therapy Corpus | We introduce a model for the linguistic hedges `very' and `quite' within the label semantics framework, and combined with the prototype and conceptual spaces theories of concepts. The proposed model emerges naturally from the representational framework we use and as such, has a clear semantic grounding. We give general... | The modelling of natural language relies on the idea that languages are compositional, i.e. that the meaning of a sentence is a function of the meanings of the words in the sentence, as proposed by BIBREF0 . Whether or not this principle tells the whole story, it is certainly important as we undoubtedly manage to creat... |
795 | Can the model add new relations to the knowledge graph, or just new entities? | The model does not add new relations to the knowledge graph. | Most current language modeling techniques only exploit co-occurrence, semantic and syntactic information from the sequence of words. However, a range of information such as the state of the speaker and dynamics of the interaction might be useful. In this work we derive motivation from psycholinguistics and propose the ... | Recurrent neural network language models (RNNLM) can theoretically model the word history over an arbitrarily long length of time and thus have been shown to perform better than traditional n-gram models BIBREF0. Recent prior work has continuously improved the performance of RNNLMs through hyper-parameter tuning, train... |
800 | What is the training and test data used? | Tweets related to a Bank of America DDos attack were used as training data. The test datasets contain tweets related to attacks to Bank of America, PNC and Wells Fargo. | Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved issues. In this paper, we present a personalized end-to-end model in an attempt to leverage personalization in goal-oriented dialogs. We first ... | There has been growing research interest in training dialog systems with end-to-end models BIBREF0 , BIBREF1 , BIBREF2 in recent years. These models are directly trained on past dialogs, without assumptions on the domain or dialog state structure BIBREF3 . One of their limitations is that they select responses only acc... |
802 | What writing styles are present in the corpus? | current news, historical news, free time, sports, juridical news pieces, personal adverts, editorials. | In this paper, we present a dataset containing 9,973 tweets related to the MeToo movement that were manually annotated for five different linguistic aspects: relevance, stance, hate speech, sarcasm, and dialogue acts. We present a detailed account of the data collection and annotation processes. The annotations have a ... | Over the last couple of years, the MeToo movement has facilitated several discussions about sexual abuse. Social media, especially Twitter, was one of the leading platforms where people shared their experiences of sexual harassment, expressed their opinions, and also offered support to victims. A large portion of these... |
806 | What meta-information is being transferred? | high-order representation of a relation, loss gradient of relation meta | The ever-growing datasets published on Linked Open Data mainly contain encyclopedic information. However, there is a lack of quality structured and semantically annotated datasets extracted from unstructured real-time sources. In this paper, we present principles for developing a knowledge graph of interlinked events u... | Several successful efforts have led to publishing huge RDF (Resource Description Framework) datasets on Linked Open Data (LOD) such as DBpedia BIBREF0 and LinkedGeoData BIBREF1 . However, these sources are limited to either structured or semi-structured data. So far, a significant portion of the Web content consists of... |
809 | How much is performance hurt when using too small amount of layers in encoder? | comparing to the results from reducing the number of layers in the decoder, the BLEU score was 69.93 which is less than 1% in case of test2016 and in case of test2017 it was less by 0.2 %. In terms of TER it had higher score by 0.7 in case of test2016 and 0.1 in case of test2017. | While many methods for learning vector space embeddings have been proposed in the field of Natural Language Processing, these methods typically do not distinguish between categories and individuals. Intuitively, if individuals are represented as vectors, we can think of categories as (soft) regions in the embedding spa... | Vector space embeddings are commonly used to represent entities in fields such as machine learning (ML) BIBREF0, natural language processing (NLP) BIBREF1, information retrieval (IR) BIBREF2 and cognitive science BIBREF3. An important point, however, is that such representations usually represent both individuals and c... |
810 | What neural machine translation models can learn in terms of transfer learning? | Multilingual Neural Machine Translation Models | In automatic post-editing (APE) it makes sense to condition post-editing (pe) decisions on both the source (src) and the machine translated text (mt) as input. This has led to multi-source encoder based APE approaches. A research challenge now is the search for architectures that best support the capture, preparation a... | The performance of state-of-the-art MT systems is not perfect, thus, human interventions are still required to correct machine translated texts into publishable quality translations BIBREF0. Automatic post-editing (APE) is a method that aims to automatically correct errors made by MT systems before performing actual hu... |
819 | How does the semi-automatic construction process work? | Automatic transcription of 5000 tokens through sequential neural models trained on the annotated part of the corpus | The complex organization of syntax in hierarchical structures is one of the core design features of human language. Duality of patterning refers for instance to the organization of the meaningful elements in a language at two distinct levels: a combinatorial level where meaningless forms are combined into meaningful fo... | In a seminal paper, Charles Hockett BIBREF0 identified duality of patterning as one of the core design features of human language. A language exhibits duality of patterning when it is organized at two distinct levels. At a first level, meaningless forms (typically referred to as phonemes) are combined into meaningful u... |
823 | What does "explicitly leverages their probabilistic correlation to guide the training process of both models" mean? | The framework jointly learns parametrized QA and QG models subject to the constraint in equation 2. In more detail, they minimize QA and QG loss functions, with a third dual loss for regularization. | This paper is concerned with open-domain question answering (i.e., OpenQA). Recently, some works have viewed this problem as a reading comprehension (RC) task, and directly applied successful RC models to it. However, the performances of such models are not so good as that in the RC task. In our opinion, the perspectiv... | Open-domain question answering (OpenQA) aims to seek answers for a broad range of questions from a large knowledge sources, e.g., structured knowledge bases BIBREF0 , BIBREF1 and unstructured documents from search engine BIBREF2 . In this paper we focus on the OpenQA task with the unstructured knowledge sources retriev... |
833 | What is WNGT 2019 shared task? | efficiency task aimed at reducing the number of parameters while minimizing drop in performance | The success of neural summarization models stems from the meticulous encodings of source articles. To overcome the impediments of limited and sometimes noisy training data, one promising direction is to make better use of the available training data by applying filters during summarization. In this paper, we propose a ... | Abstractive summarization aims to shorten a source article or paragraph by rewriting while preserving the main idea. Due to the difficulties in rewriting long documents, a large body of research on this topic has focused on paragraph-level article summarization. Among them, sequence-to-sequence models have become the m... |
837 | Was any variation in results observed based on language typology? | It is observed some variability - but not significant. Bert does not seem to gain much more syntax information than with type level information. | The role of social media, in particular microblogging platforms such as Twitter, as a conduit for actionable and tactical information during disasters is increasingly acknowledged. However, time-critical analysis of big crisis data on social media streams brings challenges to machine learning techniques, especially the... | Time-critical analysis of social media data streams is important for many application areas. For instance, responders to humanitarian disasters (e.g., earthquake, flood) need information about the disasters to determine what help is needed and where. This information usually breaks out on social media before other sour... |
843 | Can the approach be generalized to other technical domains as well? | There is no reason to think that this approach wouldn't also be successful for other technical domains. Technical terms are replaced with tokens, therefore so as long as there is a corresponding process for identifying and replacing technical terms in the new domain this approach could be viable. | Existing approaches to automatic summarization assume that a length limit for the summary is given, and view content selection as an optimization problem to maximize informativeness and minimize redundancy within this budget. This framework ignores the fact that human-written summaries have rich internal structure whic... | Writing a summary is a different task compared to producing a longer article. As a consequence, it is likely that the topic and discourse moves made in summaries differ from those in regular articles. In this work, we present a powerful extractive summarization system which exploits rich summary-internal structure to p... |
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