ACL
Collection
Accepted papers for ACL (Annual Meeting of the Association for Computational Linguistics), one dataset per year. • 14 items • Updated
paper_id stringlengths 8 8 | title stringlengths 19 150 | paper_url stringlengths 34 34 | authors listlengths 1 16 | abstract large_stringlengths 344 1.88k | anthology_id stringlengths 8 8 | doi stringlengths 20 20 | award stringclasses 0
values | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|
P18-1001 | Probabilistic FastText for Multi-Sense Word Embeddings | https://aclanthology.org/P18-1001/ | [
"Ben Athiwaratkun",
"Andrew Wilson",
"Anima Anandkumar"
] | We introduce Probabilistic FastText, a new model for word embeddings that can capture multiple word senses, sub-word structure, and uncertainty information. In particular, we represent each word with a Gaussian mixture density, where the mean of a mixture component is given by the sum of n-grams. This representation al... | P18-1001 | 10.18653/v1/P18-1001 | null | 1806.02901 | title_snapshot |
P18-1002 | A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors | https://aclanthology.org/P18-1002/ | [
"Mikhail Khodak",
"Nikunj Saunshi",
"Yingyu Liang",
"Tengyu Ma",
"Brandon Stewart",
"Sanjeev Arora"
] | Motivations like domain adaptation, transfer learning, and feature learning have fueled interest in inducing embeddings for rare or unseen words, n-grams, synsets, and other textual features. This paper introduces a la carte embedding, a simple and general alternative to the usual word2vec-based approaches for building... | P18-1002 | 10.18653/v1/P18-1002 | null | 1805.05388 | title_snapshot |
P18-1003 | Unsupervised Learning of Distributional Relation Vectors | https://aclanthology.org/P18-1003/ | [
"Shoaib Jameel",
"Zied Bouraoui",
"Steven Schockaert"
] | Word embedding models such as GloVe rely on co-occurrence statistics to learn vector representations of word meaning. While we may similarly expect that co-occurrence statistics can be used to capture rich information about the relationships between different words, existing approaches for modeling such relationships a... | P18-1003 | 10.18653/v1/P18-1003 | null | null | null |
P18-1004 | Explicit Retrofitting of Distributional Word Vectors | https://aclanthology.org/P18-1004/ | [
"Goran Glavaš",
"Ivan Vulić"
] | Semantic specialization of distributional word vectors, referred to as retrofitting, is a process of fine-tuning word vectors using external lexical knowledge in order to better embed some semantic relation. Existing retrofitting models integrate linguistic constraints directly into learning objectives and, consequentl... | P18-1004 | 10.18653/v1/P18-1004 | null | null | null |
P18-1005 | Unsupervised Neural Machine Translation with Weight Sharing | https://aclanthology.org/P18-1005/ | [
"Zhen Yang",
"Wei Chen",
"Feng Wang",
"Bo Xu"
] | Unsupervised neural machine translation (NMT) is a recently proposed approach for machine translation which aims to train the model without using any labeled data. The models proposed for unsupervised NMT often use only one shared encoder to map the pairs of sentences from different languages to a shared-latent space, ... | P18-1005 | 10.18653/v1/P18-1005 | null | 1804.09057 | title_snapshot |
P18-1006 | Triangular Architecture for Rare Language Translation | https://aclanthology.org/P18-1006/ | [
"Shuo Ren",
"Wenhu Chen",
"Shujie Liu",
"Mu Li",
"Ming Zhou",
"Shuai Ma"
] | Neural Machine Translation (NMT) performs poor on the low-resource language pair (X,Z), especially when Z is a rare language. By introducing another rich language Y, we propose a novel triangular training architecture (TA-NMT) to leverage bilingual data (Y,Z) (may be small) and (X,Y) (can be rich) to improve the transl... | P18-1006 | 10.18653/v1/P18-1006 | null | 1805.04813 | title_snapshot |
P18-1007 | Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates | https://aclanthology.org/P18-1007/ | [
"Taku Kudo"
] | Subword units are an effective way to alleviate the open vocabulary problems in neural machine translation (NMT). While sentences are usually converted into unique subword sequences, subword segmentation is potentially ambiguous and multiple segmentations are possible even with the same vocabulary. The question address... | P18-1007 | 10.18653/v1/P18-1007 | null | 1804.10959 | title_snapshot |
P18-1008 | The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation | https://aclanthology.org/P18-1008/ | [
"Mia Xu Chen",
"Orhan Firat",
"Ankur Bapna",
"Melvin Johnson",
"Wolfgang Macherey",
"George Foster",
"Llion Jones",
"Mike Schuster",
"Noam Shazeer",
"Niki Parmar",
"Ashish Vaswani",
"Jakob Uszkoreit",
"Lukasz Kaiser",
"Zhifeng Chen",
"Yonghui Wu",
"Macduff Hughes"
] | The past year has witnessed rapid advances in sequence-to-sequence (seq2seq) modeling for Machine Translation (MT). The classic RNN-based approaches to MT were first out-performed by the convolutional seq2seq model, which was then out-performed by the more recent Transformer model. Each of these new approaches consists... | P18-1008 | 10.18653/v1/P18-1008 | null | 1804.09849 | title_snapshot |
P18-1009 | Ultra-Fine Entity Typing | https://aclanthology.org/P18-1009/ | [
"Eunsol Choi",
"Omer Levy",
"Yejin Choi",
"Luke Zettlemoyer"
] | We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formulation allows us to use a new type of distant supervision at large scale: head wor... | P18-1009 | 10.18653/v1/P18-1009 | null | 1807.04905 | title_snapshot |
P18-1010 | Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking | https://aclanthology.org/P18-1010/ | [
"Shikhar Murty",
"Patrick Verga",
"Luke Vilnis",
"Irena Radovanovic",
"Andrew McCallum"
] | Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchies over types and entities contained in curated ontologies. Previous attempts to incorporate hierarchical structure have yielded little bene... | P18-1010 | 10.18653/v1/P18-1010 | null | 1807.05127 | title_snapshot |
P18-1011 | Improving Knowledge Graph Embedding Using Simple Constraints | https://aclanthology.org/P18-1011/ | [
"Boyang Ding",
"Quan Wang",
"Bin Wang",
"Li Guo"
] | Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Early works performed this task via simple models developed over KG triples. Recent attempts focused on either designing more complicated triple scoring models, or incorporating extra information beyond triples. This paper, b... | P18-1011 | 10.18653/v1/P18-1011 | null | 1805.02408 | title_snapshot |
P18-1012 | Towards Understanding the Geometry of Knowledge Graph Embeddings | https://aclanthology.org/P18-1012/ | [
"Chandrahas",
"Aditya Sharma",
"Partha Talukdar"
] | Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embedding methods. These KG embedding methods represent KG entities and relations as vectors in a high-dimensional space. Despite this popularity and effectiveness of KG embeddin... | P18-1012 | 10.18653/v1/P18-1012 | null | null | null |
P18-1013 | A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss | https://aclanthology.org/P18-1013/ | [
"Wan-Ting Hsu",
"Chieh-Kai Lin",
"Ming-Ying Lee",
"Kerui Min",
"Jing Tang",
"Min Sun"
] | We propose a unified model combining the strength of extractive and abstractive summarization. On the one hand, a simple extractive model can obtain sentence-level attention with high ROUGE scores but less readable. On the other hand, a more complicated abstractive model can obtain word-level dynamic attention to gener... | P18-1013 | 10.18653/v1/P18-1013 | null | 1805.06266 | title_snapshot |
P18-1014 | Extractive Summarization with SWAP-NET: Sentences and Words from Alternating Pointer Networks | https://aclanthology.org/P18-1014/ | [
"Aishwarya Jadhav",
"Vaibhav Rajan"
] | We present a new neural sequence-to-sequence model for extractive summarization called SWAP-NET (Sentences and Words from Alternating Pointer Networks). Extractive summaries comprising a salient subset of input sentences, often also contain important key words. Guided by this principle, we design SWAP-NET that models t... | P18-1014 | 10.18653/v1/P18-1014 | null | null | null |
P18-1015 | Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization | https://aclanthology.org/P18-1015/ | [
"Ziqiang Cao",
"Wenjie Li",
"Sujian Li",
"Furu Wei"
] | Most previous seq2seq summarization systems purely depend on the source text to generate summaries, which tends to work unstably. Inspired by the traditional template-based summarization approaches, this paper proposes to use existing summaries as soft templates to guide the seq2seq model. To this end, we use a popular... | P18-1015 | 10.18653/v1/P18-1015 | null | null | null |
P18-1016 | Simple and Effective Text Simplification Using Semantic and Neural Methods | https://aclanthology.org/P18-1016/ | [
"Elior Sulem",
"Omri Abend",
"Ari Rappoport"
] | Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used ... | P18-1016 | 10.18653/v1/P18-1016 | null | 1810.05104 | title_snapshot |
P18-1017 | Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words | https://aclanthology.org/P18-1017/ | [
"Saif Mohammad"
] | Words play a central role in language and thought. Factor analysis studies have shown that the primary dimensions of meaning are valence, arousal, and dominance (VAD). We present the NRC VAD Lexicon, which has human ratings of valence, arousal, and dominance for more than 20,000 English words. We use Best–Worst Scaling... | P18-1017 | 10.18653/v1/P18-1017 | null | null | null |
P18-1018 | Comprehensive Supersense Disambiguation of English Prepositions and Possessives | https://aclanthology.org/P18-1018/ | [
"Nathan Schneider",
"Jena D. Hwang",
"Vivek Srikumar",
"Jakob Prange",
"Austin Blodgett",
"Sarah R. Moeller",
"Aviram Stern",
"Adi Bitan",
"Omri Abend"
] | Semantic relations are often signaled with prepositional or possessive marking—but extreme polysemy bedevils their analysis and automatic interpretation. We introduce a new annotation scheme, corpus, and task for the disambiguation of prepositions and possessives in English. Unlike previous approaches, our annotations ... | P18-1018 | 10.18653/v1/P18-1018 | null | 1805.04905 | title_snapshot |
P18-1019 | A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature | https://aclanthology.org/P18-1019/ | [
"Benjamin Nye",
"Junyi Jessy Li",
"Roma Patel",
"Yinfei Yang",
"Iain Marshall",
"Ani Nenkova",
"Byron Wallace"
] | We present a corpus of 5,000 richly annotated abstracts of medical articles describing clinical randomized controlled trials. Annotations include demarcations of text spans that describe the Patient population enrolled, the Interventions studied and to what they were Compared, and the Outcomes measured (the ‘PICO’ elem... | P18-1019 | 10.18653/v1/P18-1019 | null | 1806.04185 | title_snapshot |
P18-1020 | Efficient Online Scalar Annotation with Bounded Support | https://aclanthology.org/P18-1020/ | [
"Keisuke Sakaguchi",
"Benjamin Van Durme"
] | We describe a novel method for efficiently eliciting scalar annotations for dataset construction and system quality estimation by human judgments. We contrast direct assessment (annotators assign scores to items directly), online pairwise ranking aggregation (scores derive from annotator comparison of items), and a hyb... | P18-1020 | 10.18653/v1/P18-1020 | null | 1806.01170 | title_snapshot |
P18-1021 | Neural Argument Generation Augmented with Externally Retrieved Evidence | https://aclanthology.org/P18-1021/ | [
"Xinyu Hua",
"Lu Wang"
] | High quality arguments are essential elements for human reasoning and decision-making processes. However, effective argument construction is a challenging task for both human and machines. In this work, we study a novel task on automatically generating arguments of a different stance for a given statement. We propose a... | P18-1021 | 10.18653/v1/P18-1021 | null | 1805.10254 | title_snapshot |
P18-1022 | A Stylometric Inquiry into Hyperpartisan and Fake News | https://aclanthology.org/P18-1022/ | [
"Martin Potthast",
"Johannes Kiesel",
"Kevin Reinartz",
"Janek Bevendorff",
"Benno Stein"
] | We report on a comparative style analysis of hyperpartisan (extremely one-sided) news and fake news. A corpus of 1,627 articles from 9 political publishers, three each from the mainstream, the hyperpartisan left, and the hyperpartisan right, have been fact-checked by professional journalists at BuzzFeed: 97% of the 299... | P18-1022 | 10.18653/v1/P18-1022 | null | 1702.05638 | title_snapshot |
P18-1023 | Retrieval of the Best Counterargument without Prior Topic Knowledge | https://aclanthology.org/P18-1023/ | [
"Henning Wachsmuth",
"Shahbaz Syed",
"Benno Stein"
] | Given any argument on any controversial topic, how to counter it? This question implies the challenging retrieval task of finding the best counterargument. Since prior knowledge of a topic cannot be expected in general, we hypothesize the best counterargument to invoke the same aspects as the argument while having the ... | P18-1023 | 10.18653/v1/P18-1023 | null | null | null |
P18-1024 | LinkNBed: Multi-Graph Representation Learning with Entity Linkage | https://aclanthology.org/P18-1024/ | [
"Rakshit Trivedi",
"Bunyamin Sisman",
"Xin Luna Dong",
"Christos Faloutsos",
"Jun Ma",
"Hongyuan Zha"
] | 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 ... | P18-1024 | 10.18653/v1/P18-1024 | null | 1807.08447 | title_snapshot |
P18-1025 | Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures | https://aclanthology.org/P18-1025/ | [
"Luke Vilnis",
"Xiang Li",
"Shikhar Murty",
"Andrew McCallum"
] | Embedding methods which enforce a partial order or lattice structure over the concept space, such as Order Embeddings (OE), are a natural way to model transitive relational data (e.g. entailment graphs). However, OE learns a deterministic knowledge base, limiting expressiveness of queries and the ability to use uncerta... | P18-1025 | 10.18653/v1/P18-1025 | null | 1805.06627 | title_snapshot |
P18-1026 | Graph-to-Sequence Learning using Gated Graph Neural Networks | https://aclanthology.org/P18-1026/ | [
"Daniel Beck",
"Gholamreza Haffari",
"Trevor Cohn"
] | Many NLP applications can be framed as a graph-to-sequence learning problem. Previous work proposing neural architectures on graph-to-sequence obtained promising results compared to grammar-based approaches but still rely on linearisation heuristics and/or standard recurrent networks to achieve the best performance. In... | P18-1026 | 10.18653/v1/P18-1026 | null | 1806.09835 | title_snapshot |
P18-1027 | Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context | https://aclanthology.org/P18-1027/ | [
"Urvashi Khandelwal",
"He He",
"Peng Qi",
"Dan Jurafsky"
] | We know very little about how neural language models (LM) use prior linguistic context. In this paper, we investigate the role of context in an LSTM LM, through ablation studies. Specifically, we analyze the increase in perplexity when prior context words are shuffled, replaced, or dropped. On two standard datasets, Pe... | P18-1027 | 10.18653/v1/P18-1027 | null | 1805.04623 | title_snapshot |
P18-1028 | Bridging CNNs, RNNs, and Weighted Finite-State Machines | https://aclanthology.org/P18-1028/ | [
"Roy Schwartz",
"Sam Thomson",
"Noah A. Smith"
] | Recurrent and convolutional neural networks comprise two distinct families of models that have proven to be useful for encoding natural language utterances. In this paper we present SoPa, a new model that aims to bridge these two approaches. SoPa combines neural representation learning with weighted finite-state automa... | P18-1028 | 10.18653/v1/P18-1028 | null | 1805.06061 | title_judge |
P18-1029 | Zero-shot Learning of Classifiers from Natural Language Quantification | https://aclanthology.org/P18-1029/ | [
"Shashank Srivastava",
"Igor Labutov",
"Tom Mitchell"
] | Humans can efficiently learn new concepts using language. We present a framework through which a set of explanations of a concept can be used to learn a classifier without access to any labeled examples. We use semantic parsing to map explanations to probabilistic assertions grounded in latent class labels and observed... | P18-1029 | 10.18653/v1/P18-1029 | null | null | null |
P18-1030 | Sentence-State LSTM for Text Representation | https://aclanthology.org/P18-1030/ | [
"Yue Zhang",
"Qi Liu",
"Linfeng Song"
] | Bi-directional LSTMs are a powerful tool for text representation. On the other hand, they have been shown to suffer various limitations due to their sequential nature. We investigate an alternative LSTM structure for encoding text, which consists of a parallel state for each word. Recurrent steps are used to perform lo... | P18-1030 | 10.18653/v1/P18-1030 | null | 1805.02474 | title_snapshot |
P18-1031 | Universal Language Model Fine-tuning for Text Classification | https://aclanthology.org/P18-1031/ | [
"Jeremy Howard",
"Sebastian Ruder"
] | Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce tech... | P18-1031 | 10.18653/v1/P18-1031 | null | 1801.06146 | title_snapshot |
P18-1032 | Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement | https://aclanthology.org/P18-1032/ | [
"Nina Poerner",
"Hinrich Schütze",
"Benjamin Roth"
] | The behavior of deep neural networks (DNNs) is hard to understand. This makes it necessary to explore post hoc explanation methods. We conduct the first comprehensive evaluation of explanation methods for NLP. To this end, we design two novel evaluation paradigms that cover two important classes of NLP problems: small ... | P18-1032 | 10.18653/v1/P18-1032 | null | 1801.06422 | title_judge |
P18-1033 | Improving Text-to-SQL Evaluation Methodology | https://aclanthology.org/P18-1033/ | [
"Catherine Finegan-Dollak",
"Jonathan K. Kummerfeld",
"Li Zhang",
"Karthik Ramanathan",
"Sesh Sadasivam",
"Rui Zhang",
"Dragomir Radev"
] | To be informative, an evaluation must measure how well systems generalize to realistic unseen data. We identify limitations of and propose improvements to current evaluations of text-to-SQL systems. First, we compare human-generated and automatically generated questions, characterizing properties of queries necessary f... | P18-1033 | 10.18653/v1/P18-1033 | null | 1806.09029 | title_snapshot |
P18-1034 | Semantic Parsing with Syntax- and Table-Aware SQL Generation | https://aclanthology.org/P18-1034/ | [
"Yibo Sun",
"Duyu Tang",
"Nan Duan",
"Jianshu Ji",
"Guihong Cao",
"Xiaocheng Feng",
"Bing Qin",
"Ting Liu",
"Ming Zhou"
] | We present a generative model to map natural language questions into SQL queries. Existing neural network based approaches typically generate a SQL query word-by-word, however, a large portion of the generated results is incorrect or not executable due to the mismatch between question words and table contents. Our appr... | P18-1034 | 10.18653/v1/P18-1034 | null | 1804.08338 | title_snapshot |
P18-1035 | Multitask Parsing Across Semantic Representations | https://aclanthology.org/P18-1035/ | [
"Daniel Hershcovich",
"Omri Abend",
"Ari Rappoport"
] | The ability to consolidate information of different types is at the core of intelligence, and has tremendous practical value in allowing learning for one task to benefit from generalizations learned for others. In this paper we tackle the challenging task of improving semantic parsing performance, taking UCCA parsing a... | P18-1035 | 10.18653/v1/P18-1035 | null | 1805.00287 | title_snapshot |
P18-1036 | Character-Level Models versus Morphology in Semantic Role Labeling | https://aclanthology.org/P18-1036/ | [
"Gözde Gül Şahin",
"Mark Steedman"
] | Character-level models have become a popular approach specially for their accessibility and ability to handle unseen data. However, little is known on their ability to reveal the underlying morphological structure of a word, which is a crucial skill for high-level semantic analysis tasks, such as semantic role labeling... | P18-1036 | 10.18653/v1/P18-1036 | null | 1805.11937 | title_snapshot |
P18-1037 | AMR Parsing as Graph Prediction with Latent Alignment | https://aclanthology.org/P18-1037/ | [
"Chunchuan Lyu",
"Ivan Titov"
] | Abstract meaning representations (AMRs) are broad-coverage sentence-level semantic representations. AMRs represent sentences as rooted labeled directed acyclic graphs. AMR parsing is challenging partly due to the lack of annotated alignments between nodes in the graphs and words in the corresponding sentences. We intro... | P18-1037 | 10.18653/v1/P18-1037 | null | 1805.05286 | title_snapshot |
P18-1038 | Accurate SHRG-Based Semantic Parsing | https://aclanthology.org/P18-1038/ | [
"Yufei Chen",
"Weiwei Sun",
"Xiaojun Wan"
] | We demonstrate that an SHRG-based parser can produce semantic graphs much more accurately than previously shown, by relating synchronous production rules to the syntacto-semantic composition process. Our parser achieves an accuracy of 90.35 for EDS (89.51 for DMRS) in terms of elementary dependency match, which is a 4.... | P18-1038 | 10.18653/v1/P18-1038 | null | null | null |
P18-1039 | Using Intermediate Representations to Solve Math Word Problems | https://aclanthology.org/P18-1039/ | [
"Danqing Huang",
"Jin-Ge Yao",
"Chin-Yew Lin",
"Qingyu Zhou",
"Jian Yin"
] | To solve math word problems, previous statistical approaches attempt at learning a direct mapping from a problem description to its corresponding equation system. However, such mappings do not include the information of a few higher-order operations that cannot be explicitly represented in equations but are required to... | P18-1039 | 10.18653/v1/P18-1039 | null | null | null |
P18-1040 | Discourse Representation Structure Parsing | https://aclanthology.org/P18-1040/ | [
"Jiangming Liu",
"Shay B. Cohen",
"Mirella Lapata"
] | We introduce an open-domain neural semantic parser which generates formal meaning representations in the style of Discourse Representation Theory (DRT; Kamp and Reyle 1993). We propose a method which transforms Discourse Representation Structures (DRSs) to trees and develop a structure-aware model which decomposes the ... | P18-1040 | 10.18653/v1/P18-1040 | null | null | null |
P18-1041 | Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms | https://aclanthology.org/P18-1041/ | [
"Dinghan Shen",
"Guoyin Wang",
"Wenlin Wang",
"Martin Renqiang Min",
"Qinliang Su",
"Yizhe Zhang",
"Chunyuan Li",
"Ricardo Henao",
"Lawrence Carin"
] | Many deep learning architectures have been proposed to model the compositionality in text sequences, requiring substantial number of parameters and expensive computations. However, there has not been a rigorous evaluation regarding the added value of sophisticated compositional functions. In this paper, we conduct a po... | P18-1041 | 10.18653/v1/P18-1041 | null | 1805.09843 | title_snapshot |
P18-1042 | ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations | https://aclanthology.org/P18-1042/ | [
"John Wieting",
"Kevin Gimpel"
] | We describe ParaNMT-50M, a dataset of more than 50 million English-English sentential paraphrase pairs. We generated the pairs automatically by using neural machine translation to translate the non-English side of a large parallel corpus, following Wieting et al. (2017). Our hope is that ParaNMT-50M can be a valuable r... | P18-1042 | 10.18653/v1/P18-1042 | null | 1711.05732 | title_snapshot |
P18-1043 | Event2Mind: Commonsense Inference on Events, Intents, and Reactions | https://aclanthology.org/P18-1043/ | [
"Hannah Rashkin",
"Maarten Sap",
"Emily Allaway",
"Noah A. Smith",
"Yejin Choi"
] | We investigate a new commonsense inference task: given an event described in a short free-form text (“X drinks coffee in the morning”), a system reasons about the likely intents (“X wants to stay awake”) and reactions (“X feels alert”) of the event’s participants. To support this study, we construct a new crowdsourced ... | P18-1043 | 10.18653/v1/P18-1043 | null | 1805.06939 | title_snapshot |
P18-1044 | Neural Adversarial Training for Semi-supervised Japanese Predicate-argument Structure Analysis | https://aclanthology.org/P18-1044/ | [
"Shuhei Kurita",
"Daisuke Kawahara",
"Sadao Kurohashi"
] | Japanese predicate-argument structure (PAS) analysis involves zero anaphora resolution, which is notoriously difficult. To improve the performance of Japanese PAS analysis, it is straightforward to increase the size of corpora annotated with PAS. However, since it is prohibitively expensive, it is promising to take adv... | P18-1044 | 10.18653/v1/P18-1044 | null | 1806.00971 | title_snapshot |
P18-1045 | Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures | https://aclanthology.org/P18-1045/ | [
"Prafulla Kumar Choubey",
"Ruihong Huang"
] | This paper proposes a novel approach for event coreference resolution that models correlations between event coreference chains and document topical structures through an Integer Linear Programming formulation. We explicitly model correlations between the main event chains of a document with topic transition sentences,... | P18-1045 | 10.18653/v1/P18-1045 | null | null | null |
P18-1046 | DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction | https://aclanthology.org/P18-1046/ | [
"Pengda Qin",
"Weiran Xu",
"William Yang Wang"
] | Distant supervision can effectively label data for relation extraction, but suffers from the noise labeling problem. Recent works mainly perform soft bag-level noise reduction strategies to find the relatively better samples in a sentence bag, which is suboptimal compared with making a hard decision of false positive s... | P18-1046 | 10.18653/v1/P18-1046 | null | 1805.09929 | title_snapshot |
P18-1047 | Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism | https://aclanthology.org/P18-1047/ | [
"Xiangrong Zeng",
"Daojian Zeng",
"Shizhu He",
"Kang Liu",
"Jun Zhao"
] | The relational facts in sentences are often complicated. Different relational triplets may have overlaps in a sentence. We divided the sentences into three types according to triplet overlap degree, including Normal, EntityPairOverlap and SingleEntiyOverlap. Existing methods mainly focus on Normal class and fail to ext... | P18-1047 | 10.18653/v1/P18-1047 | null | null | null |
P18-1048 | Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection | https://aclanthology.org/P18-1048/ | [
"Yu Hong",
"Wenxuan Zhou",
"Jingli Zhang",
"Guodong Zhou",
"Qiaoming Zhu"
] | Due to the ability of encoding and mapping semantic information into a high-dimensional latent feature space, neural networks have been successfully used for detecting events to a certain extent. However, such a feature space can be easily contaminated by spurious features inherent in event detection. In this paper, we... | P18-1048 | 10.18653/v1/P18-1048 | null | null | null |
P18-1049 | Context-Aware Neural Model for Temporal Information Extraction | https://aclanthology.org/P18-1049/ | [
"Yuanliang Meng",
"Anna Rumshisky"
] | We propose a context-aware neural network model for temporal information extraction. This model has a uniform architecture for event-event, event-timex and timex-timex pairs. A Global Context Layer (GCL), inspired by Neural Turing Machine (NTM), stores processed temporal relations in narrative order, and retrieves them... | P18-1049 | 10.18653/v1/P18-1049 | null | null | null |
P18-1050 | Temporal Event Knowledge Acquisition via Identifying Narratives | https://aclanthology.org/P18-1050/ | [
"Wenlin Yao",
"Ruihong Huang"
] | Inspired by the double temporality characteristic of narrative texts, we propose a novel approach for acquiring rich temporal “before/after” event knowledge across sentences in narrative stories. The double temporality states that a narrative story often describes a sequence of events following the chronological order ... | P18-1050 | 10.18653/v1/P18-1050 | null | 1805.10956 | title_snapshot |
P18-1051 | Textual Deconvolution Saliency (TDS) : a deep tool box for linguistic analysis | https://aclanthology.org/P18-1051/ | [
"Laurent Vanni",
"Melanie Ducoffe",
"Carlos Aguilar",
"Frederic Precioso",
"Damon Mayaffre"
] | In this paper, we propose a new strategy, called Text Deconvolution Saliency (TDS), to visualize linguistic information detected by a CNN for text classification. We extend Deconvolution Networks to text in order to present a new perspective on text analysis to the linguistic community. We empirically demonstrated the ... | P18-1051 | 10.18653/v1/P18-1051 | null | null | null |
P18-1052 | Coherence Modeling of Asynchronous Conversations: A Neural Entity Grid Approach | https://aclanthology.org/P18-1052/ | [
"Shafiq Joty",
"Muhammad Tasnim Mohiuddin",
"Dat Tien Nguyen"
] | We propose a novel coherence model for written asynchronous conversations (e.g., forums, emails), and show its applications in coherence assessment and thread reconstruction tasks. We conduct our research in two steps. First, we propose improvements to the recently proposed neural entity grid model by lexicalizing its ... | P18-1052 | 10.18653/v1/P18-1052 | null | 1805.02275 | title_snapshot |
P18-1053 | Deep Reinforcement Learning for Chinese Zero Pronoun Resolution | https://aclanthology.org/P18-1053/ | [
"Qingyu Yin",
"Yu Zhang",
"Wei-Nan Zhang",
"Ting Liu",
"William Yang Wang"
] | Recent neural network models for Chinese zero pronoun resolution gain great performance by capturing semantic information for zero pronouns and candidate antecedents, but tend to be short-sighted, operating solely by making local decisions. They typically predict coreference links between the zero pronoun and one singl... | P18-1053 | 10.18653/v1/P18-1053 | null | 1806.03711 | title_snapshot |
P18-1054 | Entity-Centric Joint Modeling of Japanese Coreference Resolution and Predicate Argument Structure Analysis | https://aclanthology.org/P18-1054/ | [
"Tomohide Shibata",
"Sadao Kurohashi"
] | Predicate argument structure analysis is a task of identifying structured events. To improve this field, we need to identify a salient entity, which cannot be identified without performing coreference resolution and predicate argument structure analysis simultaneously. This paper presents an entity-centric joint model ... | P18-1054 | 10.18653/v1/P18-1054 | null | null | null |
P18-1055 | Constraining MGbank: Agreement, L-Selection and Supertagging in Minimalist Grammars | https://aclanthology.org/P18-1055/ | [
"John Torr"
] | This paper reports on two strategies that have been implemented for improving the efficiency and precision of wide-coverage Minimalist Grammar (MG) parsing. The first extends the formalism presented in Torr and Stabler (2016) with a mechanism for enforcing fine-grained selectional restrictions and agreements. The secon... | P18-1055 | 10.18653/v1/P18-1055 | null | null | null |
P18-1056 | Not that much power: Linguistic alignment is influenced more by low-level linguistic features rather than social power | https://aclanthology.org/P18-1056/ | [
"Yang Xu",
"Jeremy Cole",
"David Reitter"
] | Linguistic alignment between dialogue partners has been claimed to be affected by their relative social power. A common finding has been that interlocutors of higher power tend to receive more alignment than those of lower power. However, these studies overlook some low-level linguistic features that can also affect al... | P18-1056 | 10.18653/v1/P18-1056 | null | null | null |
P18-1057 | TutorialBank: A Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation | https://aclanthology.org/P18-1057/ | [
"Alexander Fabbri",
"Irene Li",
"Prawat Trairatvorakul",
"Yijiao He",
"Weitai Ting",
"Robert Tung",
"Caitlin Westerfield",
"Dragomir Radev"
] | The field of Natural Language Processing (NLP) is growing rapidly, with new research published daily along with an abundance of tutorials, codebases and other online resources. In order to learn this dynamic field or stay up-to-date on the latest research, students as well as educators and researchers must constantly s... | P18-1057 | 10.18653/v1/P18-1057 | null | 1805.04617 | title_snapshot |
P18-1058 | Give Me More Feedback: Annotating Argument Persuasiveness and Related Attributes in Student Essays | https://aclanthology.org/P18-1058/ | [
"Winston Carlile",
"Nishant Gurrapadi",
"Zixuan Ke",
"Vincent Ng"
] | While argument persuasiveness is one of the most important dimensions of argumentative essay quality, it is relatively little studied in automated essay scoring research. Progress on scoring argument persuasiveness is hindered in part by the scarcity of annotated corpora. We present the first corpus of essays that are ... | P18-1058 | 10.18653/v1/P18-1058 | null | null | null |
P18-1059 | Inherent Biases in Reference-based Evaluation for Grammatical Error Correction | https://aclanthology.org/P18-1059/ | [
"Leshem Choshen",
"Omri Abend"
] | The prevalent use of too few references for evaluating text-to-text generation is known to bias estimates of their quality (henceforth, low coverage bias or LCB). This paper shows that overcoming LCB in Grammatical Error Correction (GEC) evaluation cannot be attained by re-scaling or by increasing the number of referen... | P18-1059 | 10.18653/v1/P18-1059 | null | 1804.11254 | title_judge |
P18-1060 | The price of debiasing automatic metrics in natural language evalaution | https://aclanthology.org/P18-1060/ | [
"Arun Chaganty",
"Stephen Mussmann",
"Percy Liang"
] | For evaluating generation systems, automatic metrics such as BLEU cost nothing to run but have been shown to correlate poorly with human judgment, leading to systematic bias against certain model improvements. On the other hand, averaging human judgments, the unbiased gold standard, is often too expensive. In this pape... | P18-1060 | 10.18653/v1/P18-1060 | null | 1807.02202 | title_judge |
P18-1061 | Neural Document Summarization by Jointly Learning to Score and Select Sentences | https://aclanthology.org/P18-1061/ | [
"Qingyu Zhou",
"Nan Yang",
"Furu Wei",
"Shaohan Huang",
"Ming Zhou",
"Tiejun Zhao"
] | Sentence scoring and sentence selection are two main steps in extractive document summarization systems. However, previous works treat them as two separated subtasks. In this paper, we present a novel end-to-end neural network framework for extractive document summarization by jointly learning to score and select sente... | P18-1061 | 10.18653/v1/P18-1061 | null | 1807.02305 | title_snapshot |
P18-1062 | Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization | https://aclanthology.org/P18-1062/ | [
"Guokan Shang",
"Wensi Ding",
"Zekun Zhang",
"Antoine Tixier",
"Polykarpos Meladianos",
"Michalis Vazirgiannis",
"Jean-Pierre Lorré"
] | We introduce a novel graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on any annotations. Our work combines the strengths of multiple recent approaches while addressing their weaknesses. Moreover, we leverage recent advances in word embeddings and graph deg... | P18-1062 | 10.18653/v1/P18-1062 | null | 1805.05271 | title_snapshot |
P18-1063 | Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting | https://aclanthology.org/P18-1063/ | [
"Yen-Chun Chen",
"Mohit Bansal"
] | Inspired by how humans summarize long documents, we propose an accurate and fast summarization model that first selects salient sentences and then rewrites them abstractively (i.e., compresses and paraphrases) to generate a concise overall summary. We use a novel sentence-level policy gradient method to bridge the non-... | P18-1063 | 10.18653/v1/P18-1063 | null | 1805.11080 | title_snapshot |
P18-1064 | Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation | https://aclanthology.org/P18-1064/ | [
"Han Guo",
"Ramakanth Pasunuru",
"Mohit Bansal"
] | An accurate abstractive summary of a document should contain all its salient information and should be logically entailed by the input document. We improve these important aspects of abstractive summarization via multi-task learning with the auxiliary tasks of question generation and entailment generation, where the fo... | P18-1064 | 10.18653/v1/P18-1064 | null | 1805.11004 | title_snapshot |
P18-1065 | Modeling and Prediction of Online Product Review Helpfulness: A Survey | https://aclanthology.org/P18-1065/ | [
"Gerardo Ocampo Diaz",
"Vincent Ng"
] | As the amount of free-form user-generated reviews in e-commerce websites continues to increase, there is an increasing need for automatic mechanisms that sift through the vast amounts of user reviews and identify quality content. Review helpfulness modeling is a task which studies the mechanisms that affect review help... | P18-1065 | 10.18653/v1/P18-1065 | null | null | null |
P18-1066 | Mining Cross-Cultural Differences and Similarities in Social Media | https://aclanthology.org/P18-1066/ | [
"Bill Yuchen Lin",
"Frank F. Xu",
"Kenny Zhu",
"Seung-won Hwang"
] | Cross-cultural differences and similarities are common in cross-lingual natural language understanding, especially for research in social media. For instance, people of distinct cultures often hold different opinions on a single named entity. Also, understanding slang terms across languages requires knowledge of cross-... | P18-1066 | 10.18653/v1/P18-1066 | null | null | null |
P18-1067 | Classification of Moral Foundations in Microblog Political Discourse | https://aclanthology.org/P18-1067/ | [
"Kristen Johnson",
"Dan Goldwasser"
] | Previous works in computer science, as well as political and social science, have shown correlation in text between political ideologies and the moral foundations expressed within that text. Additional work has shown that policy frames, which are used by politicians to bias the public towards their stance on an issue, ... | P18-1067 | 10.18653/v1/P18-1067 | null | null | null |
P18-1068 | Coarse-to-Fine Decoding for Neural Semantic Parsing | https://aclanthology.org/P18-1068/ | [
"Li Dong",
"Mirella Lapata"
] | Semantic parsing aims at mapping natural language utterances into structured meaning representations. In this work, we propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages. Given an input utterance, we first generate a rough sketch of its meaning, where low-level i... | P18-1068 | 10.18653/v1/P18-1068 | null | 1805.04793 | title_snapshot |
P18-1069 | Confidence Modeling for Neural Semantic Parsing | https://aclanthology.org/P18-1069/ | [
"Li Dong",
"Chris Quirk",
"Mirella Lapata"
] | In this work we focus on confidence modeling for neural semantic parsers which are built upon sequence-to-sequence models. We outline three major causes of uncertainty, and design various metrics to quantify these factors. These metrics are then used to estimate confidence scores that indicate whether model predictions... | P18-1069 | 10.18653/v1/P18-1069 | null | 1805.04604 | title_snapshot |
P18-1070 | StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing | https://aclanthology.org/P18-1070/ | [
"Pengcheng Yin",
"Chunting Zhou",
"Junxian He",
"Graham Neubig"
] | Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures. Annotating NL utterances with their corresponding MRs is expensive and time-consuming, and thus the limited availability of labeled data often becomes the bott... | P18-1070 | 10.18653/v1/P18-1070 | null | 1806.07832 | title_snapshot |
P18-1071 | Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing | https://aclanthology.org/P18-1071/ | [
"Bo Chen",
"Le Sun",
"Xianpei Han"
] | This paper proposes a neural semantic parsing approach – Sequence-to-Action, which models semantic parsing as an end-to-end semantic graph generation process. Our method simultaneously leverages the advantages from two recent promising directions of semantic parsing. Firstly, our model uses a semantic graph to represen... | P18-1071 | 10.18653/v1/P18-1071 | null | 1809.00773 | title_snapshot |
P18-1072 | On the Limitations of Unsupervised Bilingual Dictionary Induction | https://aclanthology.org/P18-1072/ | [
"Anders Søgaard",
"Sebastian Ruder",
"Ivan Vulić"
] | Unsupervised machine translation - i.e., not assuming any cross-lingual supervision signal, whether a dictionary, translations, or comparable corpora - seems impossible, but nevertheless, Lample et al. (2017) recently proposed a fully unsupervised machine translation (MT) model. The model relies heavily on an adversari... | P18-1072 | 10.18653/v1/P18-1072 | null | 1805.03620 | title_snapshot |
P18-1073 | A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings | https://aclanthology.org/P18-1073/ | [
"Mikel Artetxe",
"Gorka Labaka",
"Eneko Agirre"
] | Recent work has managed to learn cross-lingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training. However, their evaluation has focused on favorable conditions, using comparable corpora or closely-related languages, and we show that they often fail in... | P18-1073 | 10.18653/v1/P18-1073 | null | 1805.06297 | title_snapshot |
P18-1074 | A Multi-lingual Multi-task Architecture for Low-resource Sequence Labeling | https://aclanthology.org/P18-1074/ | [
"Ying Lin",
"Shengqi Yang",
"Veselin Stoyanov",
"Heng Ji"
] | We propose a multi-lingual multi-task architecture to develop supervised models with a minimal amount of labeled data for sequence labeling. In this new architecture, we combine various transfer models using two layers of parameter sharing. On the first layer, we construct the basis of the architecture to provide unive... | P18-1074 | 10.18653/v1/P18-1074 | null | null | null |
P18-1075 | Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable | https://aclanthology.org/P18-1075/ | [
"Viktor Hangya",
"Fabienne Braune",
"Alexander Fraser",
"Hinrich Schütze"
] | Bilingual tasks, such as bilingual lexicon induction and cross-lingual classification, are crucial for overcoming data sparsity in the target language. Resources required for such tasks are often out-of-domain, thus domain adaptation is an important problem here. We make two contributions. First, we test a delightfully... | P18-1075 | 10.18653/v1/P18-1075 | null | null | null |
P18-1076 | Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge | https://aclanthology.org/P18-1076/ | [
"Todor Mihaylov",
"Anette Frank"
] | We introduce a neural reading comprehension model that integrates external commonsense knowledge, encoded as a key-value memory, in a cloze-style setting. Instead of relying only on document-to-question interaction or discrete features as in prior work, our model attends to relevant external knowledge and combines this... | P18-1076 | 10.18653/v1/P18-1076 | null | 1805.07858 | title_snapshot |
P18-1077 | Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds | https://aclanthology.org/P18-1077/ | [
"Igor Labutov",
"Bishan Yang",
"Anusha Prakash",
"Amos Azaria"
] | Question Answering (QA), as a research field, has primarily focused on either knowledge bases (KBs) or free text as a source of knowledge. These two sources have historically shaped the kinds of questions that are asked over these sources, and the methods developed to answer them. In this work, we look towards a practi... | P18-1077 | 10.18653/v1/P18-1077 | null | 1902.09093 | title_snapshot |
P18-1078 | Simple and Effective Multi-Paragraph Reading Comprehension | https://aclanthology.org/P18-1078/ | [
"Christopher Clark",
"Matt Gardner"
] | We introduce a method of adapting neural paragraph-level question answering models to the case where entire documents are given as input. Most current question answering models cannot scale to document or multi-document input, and naively applying these models to each paragraph independently often results in them being... | P18-1078 | 10.18653/v1/P18-1078 | null | 1710.10723 | title_snapshot |
P18-1079 | Semantically Equivalent Adversarial Rules for Debugging NLP models | https://aclanthology.org/P18-1079/ | [
"Marco Tulio Ribeiro",
"Sameer Singh",
"Carlos Guestrin"
] | Complex machine learning models for NLP are often brittle, making different predictions for input instances that are extremely similar semantically. To automatically detect this behavior for individual instances, we present semantically equivalent adversaries (SEAs) – semantic-preserving perturbations that induce chang... | P18-1079 | 10.18653/v1/P18-1079 | null | null | null |
P18-1080 | Style Transfer Through Back-Translation | https://aclanthology.org/P18-1080/ | [
"Shrimai Prabhumoye",
"Yulia Tsvetkov",
"Ruslan Salakhutdinov",
"Alan W Black"
] | Style transfer is the task of rephrasing the text to contain specific stylistic properties without changing the intent or affect within the context. This paper introduces a new method for automatic style transfer. We first learn a latent representation of the input sentence which is grounded in a language translation m... | P18-1080 | 10.18653/v1/P18-1080 | null | 1804.09000 | title_snapshot |
P18-1081 | Generating Fine-Grained Open Vocabulary Entity Type Descriptions | https://aclanthology.org/P18-1081/ | [
"Rajarshi Bhowmik",
"Gerard de Melo"
] | While large-scale knowledge graphs provide vast amounts of structured facts about entities, a short textual description can often be useful to succinctly characterize an entity and its type. Unfortunately, many knowledge graphs entities lack such textual descriptions. In this paper, we introduce a dynamic memory-based ... | P18-1081 | 10.18653/v1/P18-1081 | null | 1805.10564 | title_snapshot |
P18-1082 | Hierarchical Neural Story Generation | https://aclanthology.org/P18-1082/ | [
"Angela Fan",
"Mike Lewis",
"Yann Dauphin"
] | We explore story generation: creative systems that can build coherent and fluent passages of text about a topic. We collect a large dataset of 300K human-written stories paired with writing prompts from an online forum. Our dataset enables hierarchical story generation, where the model first generates a premise, and th... | P18-1082 | 10.18653/v1/P18-1082 | null | 1805.04833 | title_snapshot |
P18-1083 | No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling | https://aclanthology.org/P18-1083/ | [
"Xin Wang",
"Wenhu Chen",
"Yuan-Fang Wang",
"William Yang Wang"
] | Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challe... | P18-1083 | 10.18653/v1/P18-1083 | null | 1804.09160 | title_snapshot |
P18-1084 | Bridging Languages through Images with Deep Partial Canonical Correlation Analysis | https://aclanthology.org/P18-1084/ | [
"Guy Rotman",
"Ivan Vulić",
"Roi Reichart"
] | We present a deep neural network that leverages images to improve bilingual text embeddings. Relying on bilingual image tags and descriptions, our approach conditions text embedding induction on the shared visual information for both languages, producing highly correlated bilingual embeddings. In particular, we propose... | P18-1084 | 10.18653/v1/P18-1084 | null | null | null |
P18-1085 | Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search | https://aclanthology.org/P18-1085/ | [
"Jamie Kiros",
"William Chan",
"Geoffrey Hinton"
] | We introduce Picturebook, a large-scale lookup operation to ground language via ‘snapshots’ of our physical world accessed through image search. For each word in a vocabulary, we extract the top-k images from Google image search and feed the images through a convolutional network to extract a word embedding. We introdu... | P18-1085 | 10.18653/v1/P18-1085 | null | null | null |
P18-1086 | What Action Causes This? Towards Naive Physical Action-Effect Prediction | https://aclanthology.org/P18-1086/ | [
"Qiaozi Gao",
"Shaohua Yang",
"Joyce Chai",
"Lucy Vanderwende"
] | Despite recent advances in knowledge representation, automated reasoning, and machine learning, artificial agents still lack the ability to understand basic action-effect relations regarding the physical world, for example, the action of cutting a cucumber most likely leads to the state where the cucumber is broken apa... | P18-1086 | 10.18653/v1/P18-1086 | null | null | null |
P18-1087 | Transformation Networks for Target-Oriented Sentiment Classification | https://aclanthology.org/P18-1087/ | [
"Xin Li",
"Lidong Bing",
"Wai Lam",
"Bei Shi"
] | Target-oriented sentiment classification aims at classifying sentiment polarities over individual opinion targets in a sentence. RNN with attention seems a good fit for the characteristics of this task, and indeed it achieves the state-of-the-art performance. After re-examining the drawbacks of attention mechanism and ... | P18-1087 | 10.18653/v1/P18-1087 | null | 1805.01086 | title_snapshot |
P18-1088 | Target-Sensitive Memory Networks for Aspect Sentiment Classification | https://aclanthology.org/P18-1088/ | [
"Shuai Wang",
"Sahisnu Mazumder",
"Bing Liu",
"Mianwei Zhou",
"Yi Chang"
] | Aspect sentiment classification (ASC) is a fundamental task in sentiment analysis. Given an aspect/target and a sentence, the task classifies the sentiment polarity expressed on the target in the sentence. Memory networks (MNs) have been used for this task recently and have achieved state-of-the-art results. In MNs, at... | P18-1088 | 10.18653/v1/P18-1088 | null | null | null |
P18-1089 | Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification | https://aclanthology.org/P18-1089/ | [
"Raksha Sharma",
"Pushpak Bhattacharyya",
"Sandipan Dandapat",
"Himanshu Sharad Bhatt"
] | Getting manually labeled data in each domain is always an expensive and a time consuming task. Cross-domain sentiment analysis has emerged as a demanding concept where a labeled source domain facilitates a sentiment classifier for an unlabeled target domain. However, polarity orientation (positive or negative) and the ... | P18-1089 | 10.18653/v1/P18-1089 | null | null | null |
P18-1090 | Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach | https://aclanthology.org/P18-1090/ | [
"Jingjing Xu",
"Xu Sun",
"Qi Zeng",
"Xiaodong Zhang",
"Xuancheng Ren",
"Houfeng Wang",
"Wenjie Li"
] | The goal of sentiment-to-sentiment “translation” is to change the underlying sentiment of a sentence while keeping its content. The main challenge is the lack of parallel data. To solve this problem, we propose a cycled reinforcement learning method that enables training on unpaired data by collaboration between a neut... | P18-1090 | 10.18653/v1/P18-1090 | null | 1805.05181 | title_snapshot |
P18-1091 | Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference | https://aclanthology.org/P18-1091/ | [
"Boyuan Pan",
"Yazheng Yang",
"Zhou Zhao",
"Yueting Zhuang",
"Deng Cai",
"Xiaofei He"
] | Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is one of the most important problems in natural language processing. It requires to infer the logical relationship between two given sentences. While current approaches mostly focus on the interaction architectures of the sentences, ... | P18-1091 | 10.18653/v1/P18-1091 | null | 1907.09692 | title_snapshot |
P18-1092 | Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module | https://aclanthology.org/P18-1092/ | [
"Juan Pavez",
"Héctor Allende",
"Héctor Allende-Cid"
] | During the last years, there has been a lot of interest in achieving some kind of complex reasoning using deep neural networks. To do that, models like Memory Networks (MemNNs) have combined external memory storages and attention mechanisms. These architectures, however, lack of more complex reasoning mechanisms that c... | P18-1092 | 10.18653/v1/P18-1092 | null | 1805.09354 | title_snapshot |
P18-1093 | Reasoning with Sarcasm by Reading In-Between | https://aclanthology.org/P18-1093/ | [
"Yi Tay",
"Anh Tuan Luu",
"Siu Cheung Hui",
"Jian Su"
] | Sarcasm is a sophisticated speech act which commonly manifests on social communities such as Twitter and Reddit. The prevalence of sarcasm on the social web is highly disruptive to opinion mining systems due to not only its tendency of polarity flipping but also usage of figurative language. Sarcasm commonly manifests ... | P18-1093 | 10.18653/v1/P18-1093 | null | 1805.02856 | title_snapshot |
P18-1094 | Adversarial Contrastive Estimation | https://aclanthology.org/P18-1094/ | [
"Avishek Joey Bose",
"Huan Ling",
"Yanshuai Cao"
] | Learning by contrasting positive and negative samples is a general strategy adopted by many methods. Noise contrastive estimation (NCE) for word embeddings and translating embeddings for knowledge graphs are examples in NLP employing this approach. In this work, we view contrastive learning as an abstraction of all suc... | P18-1094 | 10.18653/v1/P18-1094 | null | 1805.03642 | title_snapshot |
P18-1095 | Adaptive Scaling for Sparse Detection in Information Extraction | https://aclanthology.org/P18-1095/ | [
"Hongyu Lin",
"Yaojie Lu",
"Xianpei Han",
"Le Sun"
] | This paper focuses on detection tasks in information extraction, where positive instances are sparsely distributed and models are usually evaluated using F-measure on positive classes. These characteristics often result in deficient performance of neural network based detection models. In this paper, we propose adaptiv... | P18-1095 | 10.18653/v1/P18-1095 | null | 1805.00250 | title_snapshot |
P18-1096 | Strong Baselines for Neural Semi-Supervised Learning under Domain Shift | https://aclanthology.org/P18-1096/ | [
"Sebastian Ruder",
"Barbara Plank"
] | Novel neural models have been proposed in recent years for learning under domain shift. Most models, however, only evaluate on a single task, on proprietary datasets, or compare to weak baselines, which makes comparison of models difficult. In this paper, we re-evaluate classic general-purpose bootstrapping approaches ... | P18-1096 | 10.18653/v1/P18-1096 | null | 1804.09530 | title_snapshot |
P18-1097 | Fluency Boost Learning and Inference for Neural Grammatical Error Correction | https://aclanthology.org/P18-1097/ | [
"Tao Ge",
"Furu Wei",
"Ming Zhou"
] | Most of the neural sequence-to-sequence (seq2seq) models for grammatical error correction (GEC) have two limitations: (1) a seq2seq model may not be well generalized with only limited error-corrected data; (2) a seq2seq model may fail to completely correct a sentence with multiple errors through normal seq2seq inferenc... | P18-1097 | 10.18653/v1/P18-1097 | null | null | null |
P18-1098 | A Neural Architecture for Automated ICD Coding | https://aclanthology.org/P18-1098/ | [
"Pengtao Xie",
"Eric Xing"
] | The International Classification of Diseases (ICD) provides a hierarchy of diagnostic codes for classifying diseases. Medical coding – which assigns a subset of ICD codes to a patient visit – is a mandatory process that is crucial for patient care and billing. Manual coding is time-consuming, expensive, and error prone... | P18-1098 | 10.18653/v1/P18-1098 | null | null | null |
P18-1099 | Domain Adaptation with Adversarial Training and Graph Embeddings | https://aclanthology.org/P18-1099/ | [
"Firoj Alam",
"Shafiq Joty",
"Muhammad Imran"
] | The success of deep neural networks (DNNs) is heavily dependent on the availability of labeled data. However, obtaining labeled data is a big challenge in many real-world problems. In such scenarios, a DNN model can leverage labeled and unlabeled data from a related domain, but it has to deal with the shift in data dis... | P18-1099 | 10.18653/v1/P18-1099 | null | 1805.05151 | title_snapshot |
P18-1100 | TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring | https://aclanthology.org/P18-1100/ | [
"Cancan Jin",
"Ben He",
"Kai Hui",
"Le Sun"
] | Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To ... | P18-1100 | 10.18653/v1/P18-1100 | null | null | null |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- -