Paper Group NANR 25
Abstract Meaning Representation of Constructions: The More We Include, the Better the Representation. Embedding WordNet Knowledge for Textual Entailment. A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge. KOI at SemEval-2018 Task 5: Building Knowledge Graph of Incidents. WSNet: Learning Compact and Efficient Networks with …
Abstract Meaning Representation of Constructions: The More We Include, the Better the Representation
| Title | Abstract Meaning Representation of Constructions: The More We Include, the Better the Representation |
| Authors | Claire Bonial, Bianca Badarau, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Tim O{'}Gorman, Martha Palmer, Nathan Schneider |
| Abstract | |
| Tasks | Machine Translation, Question Answering |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1266/ |
| https://www.aclweb.org/anthology/L18-1266 | |
| PWC | https://paperswithcode.com/paper/abstract-meaning-representation-of |
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Embedding WordNet Knowledge for Textual Entailment
| Title | Embedding WordNet Knowledge for Textual Entailment |
| Authors | Yunshi Lan, Jing Jiang |
| Abstract | In this paper, we study how we can improve a deep learning approach to textual entailment by incorporating lexical entailment relations from WordNet. Our idea is to embed the lexical entailment knowledge contained in WordNet in specially-learned word vectors, which we call {``}entailment vectors.{''} We present a standard neural network model and a novel set-theoretic model to learn these entailment vectors from word pairs with known lexical entailment relations derived from WordNet. We further incorporate these entailment vectors into a decomposable attention model for textual entailment and evaluate the model on the SICK and the SNLI dataset. We find that using these special entailment word vectors, we can significantly improve the performance of textual entailment compared with a baseline that uses only standard word2vec vectors. The final performance of our model is close to or above the state of the art, but our method does not rely on any manually-crafted rules or extensive syntactic features. | |
| Tasks | Feature Engineering, Natural Language Inference |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1023/ |
| https://www.aclweb.org/anthology/C18-1023 | |
| PWC | https://paperswithcode.com/paper/embedding-wordnet-knowledge-for-textual |
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A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge
| Title | A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge |
| Authors | Ali Emami, Adam Trischler, Kaheer Suleman, Jackie Chi Kit Cheung |
| Abstract | We introduce an automatic system that performs well on two common-sense reasoning tasks, the Winograd Schema Challenge (WSC) and the Choice of Plausible Alternatives (COPA). Problem instances from these tasks require diverse, complex forms of inference and knowledge to solve. Our method uses a knowledge-hunting module to gather text from the web, which serves as evidence for candidate problem resolutions. Given an input problem, our system generates relevant queries to send to a search engine. It extracts and classifies knowledge from the returned results and weighs it to make a resolution. Our approach improves F1 performance on the WSC by 0.16 over the previous best and is competitive with the state-of-the-art on COPA, demonstrating its general applicability. |
| Tasks | Common Sense Reasoning, Coreference Resolution, Information Retrieval |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/N18-4004/ |
| https://www.aclweb.org/anthology/N18-4004 | |
| PWC | https://paperswithcode.com/paper/a-generalized-knowledge-hunting-framework-for |
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KOI at SemEval-2018 Task 5: Building Knowledge Graph of Incidents
| Title | KOI at SemEval-2018 Task 5: Building Knowledge Graph of Incidents |
| Authors | Paramita Mirza, Fariz Darari, Rahmad Mahendra |
| Abstract | We present KOI (Knowledge of Incidents), a system that given news articles as input, builds a knowledge graph (KOI-KG) of incidental events. KOI-KG can then be used to efficiently answer questions such {``}How many killing incidents happened in 2017 that involve Sean?{''} The required steps in building the KG include: (i) document preprocessing involving word sense disambiguation, named-entity recognition, temporal expression recognition and normalization, and semantic role labeling; (ii) incidental event extraction and coreference resolution via document clustering; and (iii) KG construction and population. | |
| Tasks | Coreference Resolution, Named Entity Recognition, Semantic Role Labeling, Word Sense Disambiguation |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1010/ |
| https://www.aclweb.org/anthology/S18-1010 | |
| PWC | https://paperswithcode.com/paper/koi-at-semeval-2018-task-5-building-knowledge |
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WSNet: Learning Compact and Efficient Networks with Weight Sampling
| Title | WSNet: Learning Compact and Efficient Networks with Weight Sampling |
| Authors | Xiaojie Jin, Yingzhen Yang, Ning Xu, Jianchao Yang, Jiashi Feng, Shuicheng Yan |
| Abstract | We present a new approach and a novel architecture, termed WSNet, for learning compact and efficient deep neural networks. Existing approaches conventionally learn full model parameters independently and then compress them via \emph{ad hoc} processing such as model pruning or filter factorization. Alternatively, WSNet proposes learning model parameters by sampling from a compact set of learnable parameters, which naturally enforces {parameter sharing} throughout the learning process. We demonstrate that such a novel weight sampling approach (and induced WSNet) promotes both weights and computation sharing favorably. By employing this method, we can more efficiently learn much smaller networks with competitive performance compared to baseline networks with equal numbers of convolution filters. Specifically, we consider learning compact and efficient 1D convolutional neural networks for audio classification. Extensive experiments on multiple audio classification datasets verify the effectiveness of WSNet. Combined with weight quantization, the resulted models are up to \textbf{180$\times$} smaller and theoretically up to \textbf{16$\times$} faster than the well-established baselines, without noticeable performance drop. |
| Tasks | Audio Classification, Quantization |
| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=H1I3M7Z0b |
| https://openreview.net/pdf?id=H1I3M7Z0b | |
| PWC | https://paperswithcode.com/paper/wsnet-learning-compact-and-efficient-networks |
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KNU CI System at SemEval-2018 Task4: Character Identification by Solving Sequence-Labeling Problem
| Title | KNU CI System at SemEval-2018 Task4: Character Identification by Solving Sequence-Labeling Problem |
| Authors | Cheoneum Park, Heejun Song, Changki Lee |
| Abstract | Character identification is an entity-linking task that finds words referring to the same person among the nouns mentioned in a conversation and turns them into one entity. In this paper, we define a sequence-labeling problem to solve character identification, and propose an attention-based recurrent neural network (RNN) encoder{–}decoder model. The in-put document for character identification on multiparty dialogues consists of several conversations, which increase the length of the input sequence. The RNN encoder{–}decoder model suffers from poor performance when the length of the input sequence is long. To solve this problem, we propose applying position encoding and the self-matching network to the RNN encoder{–}decoder model. Our experimental results demonstrate that of the four models proposed, Model 2 showed an F1 score of 86.00{%} and a label accuracy of 85.10{%} at the scene-level. |
| Tasks | Coreference Resolution, Entity Linking |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1107/ |
| https://www.aclweb.org/anthology/S18-1107 | |
| PWC | https://paperswithcode.com/paper/knu-ci-system-at-semeval-2018-task4-character |
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Exploring the Influence of Spelling Errors on Lexical Variation Measures
| Title | Exploring the Influence of Spelling Errors on Lexical Variation Measures |
| Authors | Ryo Nagata, Taisei Sato, Hiroya Takamura |
| Abstract | This paper explores the influence of spelling errors on lexical variation measures. Lexical richness measures such as Type-Token Ration (TTR) and Yule{'}s K are often used for learner English analysis and assessment. When applied to learner English, however, they can be unreliable because of the spelling errors appearing in it. Namely, they are, directly or indirectly, based on the counts of distinct word types, and spelling errors undesirably increase the number of distinct words. This paper introduces and examines the hypothesis that lexical richness measures become unstable in learner English because of spelling errors. Specifically, it tests the hypothesis on English learner corpora of three groups (middle school, high school, and college students). To be precise, it estimates the difference in TTR and Yule{'}s K caused by spelling errors, by calculating their values before and after spelling errors are manually corrected. Furthermore, it examines the results theoretically and empirically to deepen the understanding of the influence of spelling errors on them. |
| Tasks | |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1202/ |
| https://www.aclweb.org/anthology/C18-1202 | |
| PWC | https://paperswithcode.com/paper/exploring-the-influence-of-spelling-errors-on |
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Tackling the Story Ending Biases in The Story Cloze Test
| Title | Tackling the Story Ending Biases in The Story Cloze Test |
| Authors | Rishi Sharma, James Allen, Bakhsh, Omid eh, Nasrin Mostafazadeh |
| Abstract | The Story Cloze Test (SCT) is a recent framework for evaluating story comprehension and script learning. There have been a variety of models tackling the SCT so far. Although the original goal behind the SCT was to require systems to perform deep language understanding and commonsense reasoning for successful narrative understanding, some recent models could perform significantly better than the initial baselines by leveraging human-authorship biases discovered in the SCT dataset. In order to shed some light on this issue, we have performed various data analysis and analyzed a variety of top performing models presented for this task. Given the statistics we have aggregated, we have designed a new crowdsourcing scheme that creates a new SCT dataset, which overcomes some of the biases. We benchmark a few models on the new dataset and show that the top-performing model on the original SCT dataset fails to keep up its performance. Our findings further signify the importance of benchmarking NLP systems on various evolving test sets. |
| Tasks | |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-2119/ |
| https://www.aclweb.org/anthology/P18-2119 | |
| PWC | https://paperswithcode.com/paper/tackling-the-story-ending-biases-in-the-story |
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A Linked Coptic Dictionary Online
| Title | A Linked Coptic Dictionary Online |
| Authors | Frank Feder, Maxim Kupreyev, Emma Manning, Caroline T. Schroeder, Amir Zeldes |
| Abstract | We describe a new project publishing a freely available online dictionary for Coptic. The dictionary encompasses comprehensive cross-referencing mechanisms, including linking entries to an online scanned edition of Crum{'}s Coptic Dictionary, internal cross-references and etymological information, translated searchable definitions in English, French and German, and linked corpus data which provides frequencies and corpus look-up for headwords and multiword expressions. Headwords are available for linking in external projects using a REST API. We describe the challenges in encoding our dictionary using TEI XML and implementing linking mechanisms to construct a Web interface querying frequency information, which draw on NLP tools to recognize inflected forms in context. We evaluate our dictionary{'}s coverage using digital corpora of Coptic available online. |
| Tasks | |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-4502/ |
| https://www.aclweb.org/anthology/W18-4502 | |
| PWC | https://paperswithcode.com/paper/a-linked-coptic-dictionary-online |
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Did you offend me? Classification of Offensive Tweets in Hinglish Language
| Title | Did you offend me? Classification of Offensive Tweets in Hinglish Language |
| Authors | Puneet Mathur, Ramit Sawhney, Meghna Ayyar, Rajiv Shah |
| Abstract | The use of code-switched languages (\textit{e.g.}, Hinglish, which is derived by the blending of Hindi with the English language) is getting much popular on Twitter due to their ease of communication in native languages. However, spelling variations and absence of grammar rules introduce ambiguity and make it difficult to understand the text automatically. This paper presents the Multi-Input Multi-Channel Transfer Learning based model (MIMCT) to detect offensive (hate speech or abusive) Hinglish tweets from the proposed Hinglish Offensive Tweet (HOT) dataset using transfer learning coupled with multiple feature inputs. Specifically, it takes multiple primary word embedding along with secondary extracted features as inputs to train a multi-channel CNN-LSTM architecture that has been pre-trained on English tweets through transfer learning. The proposed MIMCT model outperforms the baseline supervised classification models, transfer learning based CNN and LSTM models to establish itself as the state of the art in the unexplored domain of Hinglish offensive text classification. |
| Tasks | Text Classification, Transfer Learning |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/W18-5118/ |
| https://www.aclweb.org/anthology/W18-5118 | |
| PWC | https://paperswithcode.com/paper/did-you-offend-me-classification-of-offensive |
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FEUP at SemEval-2018 Task 5: An Experimental Study of a Question Answering System
| Title | FEUP at SemEval-2018 Task 5: An Experimental Study of a Question Answering System |
| Authors | Carla Abreu, Eug{'e}nio Oliveira |
| Abstract | We present the approach developed at the Faculty of Engineering of the University of Porto to participate in SemEval-2018 Task 5: Counting Events and Participants within Highly Ambiguous Data covering a very long tail.The work described here presents the experimental system developed to extract entities from news articles for the sake of Question Answering. We propose a supervised learning approach to enable the recognition of two different types of entities: Locations and Participants. We also discuss the use of distance-based algorithms (using Levenshtein distance and Q-grams) for the detection of documents{'} closeness based on the entities extracted. For the experiments, we also used a multi-agent system that improved the performance. |
| Tasks | Named Entity Recognition, Question Answering |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1109/ |
| https://www.aclweb.org/anthology/S18-1109 | |
| PWC | https://paperswithcode.com/paper/feup-at-semeval-2018-task-5-an-experimental |
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Chahta Anumpa: A multimodal corpus of the Choctaw Language
| Title | Chahta Anumpa: A multimodal corpus of the Choctaw Language |
| Authors | Jacqueline Brixey, Eli Pincus, Ron Artstein |
| Abstract | |
| Tasks | |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1532/ |
| https://www.aclweb.org/anthology/L18-1532 | |
| PWC | https://paperswithcode.com/paper/chahta-anumpa-a-multimodal-corpus-of-the |
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Expanding Abbreviations in a Strongly Inflected Language: Are Morphosyntactic Tags Sufficient?
| Title | Expanding Abbreviations in a Strongly Inflected Language: Are Morphosyntactic Tags Sufficient? |
| Authors | Piotr {.Z}elasko |
| Abstract | |
| Tasks | Language Modelling |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1295/ |
| https://www.aclweb.org/anthology/L18-1295 | |
| PWC | https://paperswithcode.com/paper/expanding-abbreviations-in-a-strongly |
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Universal Dependencies Version 2 for Japanese
| Title | Universal Dependencies Version 2 for Japanese |
| Authors | Masayuki Asahara, Hiroshi Kanayama, Takaaki Tanaka, Yusuke Miyao, Sumire Uematsu, Shinsuke Mori, Yuji Matsumoto, Mai Omura, Yugo Murawaki |
| Abstract | |
| Tasks | |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1287/ |
| https://www.aclweb.org/anthology/L18-1287 | |
| PWC | https://paperswithcode.com/paper/universal-dependencies-version-2-for-japanese |
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An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption
| Title | An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption |
| Authors | Xiyu Yu, Tongliang Liu, Mingming Gong, Kayhan Batmanghelich, Dacheng Tao |
| Abstract | In this paper, we study the mixture proportion estimation (MPE) problem in a new setting: given samples from the mixture and the component distributions, we identify the proportions of the components in the mixture distribution. To address this problem, we make use of a linear independence assumption, i.e., the component distributions are independent from each other, which is much weaker than assumptions exploited in the previous MPE methods. Based on this assumption, we propose a method (1) that uniquely identifies the mixture proportions, (2) whose output provably converges to the optimal solution, and (3) that is computationally efficient. We show the superiority of the proposed method over the state-of-the-art methods in two applications including learning with label noise and semi-supervised learning on both synthetic and real-world datasets. |
| Tasks | |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Yu_An_Efficient_and_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Yu_An_Efficient_and_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/an-efficient-and-provable-approach-for |
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