NAACL
Collection
Accepted papers for NAACL (Annual Conference of the North American Chapter of the Association for Computational Linguistics), one dataset per year. • 9 items • Updated
paper_id stringlengths 8 8 | title stringlengths 16 142 | paper_url stringlengths 34 34 | authors listlengths 1 12 | abstract large_stringlengths 341 1.87k | anthology_id stringlengths 8 8 | doi stringlengths 20 20 | award stringclasses 5
values | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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N19-1001 | Entity Recognition at First Sight: Improving NER with Eye Movement Information | https://aclanthology.org/N19-1001/ | [
"Nora Hollenstein",
"Ce Zhang"
] | Previous research shows that eye-tracking data contains information about the lexical and syntactic properties of text, which can be used to improve natural language processing models. In this work, we leverage eye movement features from three corpora with recorded gaze information to augment a state-of-the-art neural ... | N19-1001 | 10.18653/v1/N19-1001 | null | 1902.10068 | title_snapshot |
N19-1002 | The emergence of number and syntax units in LSTM language models | https://aclanthology.org/N19-1002/ | [
"Yair Lakretz",
"German Kruszewski",
"Theo Desbordes",
"Dieuwke Hupkes",
"Stanislas Dehaene",
"Marco Baroni"
] | Recent work has shown that LSTMs trained on a generic language modeling objective capture syntax-sensitive generalizations such as long-distance number agreement. We have however no mechanistic understanding of how they accomplish this remarkable feat. Some have conjectured it depends on heuristics that do not truly ta... | N19-1002 | 10.18653/v1/N19-1002 | null | 1903.07435 | title_snapshot |
N19-1003 | Neural Self-Training through Spaced Repetition | https://aclanthology.org/N19-1003/ | [
"Hadi Amiri"
] | Self-training is a semi-supervised learning approach for utilizing unlabeled data to create better learners. The efficacy of self-training algorithms depends on their data sampling techniques. The majority of current sampling techniques are based on predetermined policies which may not effectively explore the data spac... | N19-1003 | 10.18653/v1/N19-1003 | null | null | null |
N19-1004 | Neural language models as psycholinguistic subjects: Representations of syntactic state | https://aclanthology.org/N19-1004/ | [
"Richard Futrell",
"Ethan Wilcox",
"Takashi Morita",
"Peng Qian",
"Miguel Ballesteros",
"Roger Levy"
] | We investigate the extent to which the behavior of neural network language models reflects incremental representations of syntactic state. To do so, we employ experimental methodologies which were originally developed in the field of psycholinguistics to study syntactic representation in the human mind. We examine neur... | N19-1004 | 10.18653/v1/N19-1004 | null | 1903.03260 | title_snapshot |
N19-1005 | Understanding language-elicited EEG data by predicting it from a fine-tuned language model | https://aclanthology.org/N19-1005/ | [
"Dan Schwartz",
"Tom Mitchell"
] | Electroencephalography (EEG) recordings of brain activity taken while participants read or listen to language are widely used within the cognitive neuroscience and psycholinguistics communities as a tool to study language comprehension. Several time-locked stereotyped EEG responses to word-presentations – known collect... | N19-1005 | 10.18653/v1/N19-1005 | null | 1904.01548 | title_snapshot |
N19-1006 | Pre-training on high-resource speech recognition improves low-resource speech-to-text translation | https://aclanthology.org/N19-1006/ | [
"Sameer Bansal",
"Herman Kamper",
"Karen Livescu",
"Adam Lopez",
"Sharon Goldwater"
] | We present a simple approach to improve direct speech-to-text translation (ST) when the source language is low-resource: we pre-train the model on a high-resource automatic speech recognition (ASR) task, and then fine-tune its parameters for ST. We demonstrate that our approach is effective by pre-training on 300 hours... | N19-1006 | 10.18653/v1/N19-1006 | null | 1809.01431 | title_snapshot |
N19-1007 | Measuring the perceptual availability of phonological features during language acquisition using unsupervised binary stochastic autoencoders | https://aclanthology.org/N19-1007/ | [
"Cory Shain",
"Micha Elsner"
] | In this paper, we deploy binary stochastic neural autoencoder networks as models of infant language learning in two typologically unrelated languages (Xitsonga and English). We show that the drive to model auditory percepts leads to latent clusters that partially align with theory-driven phonemic categories. We further... | N19-1007 | 10.18653/v1/N19-1007 | null | null | null |
N19-1008 | Giving Attention to the Unexpected: Using Prosody Innovations in Disfluency Detection | https://aclanthology.org/N19-1008/ | [
"Vicky Zayats",
"Mari Ostendorf"
] | Disfluencies in spontaneous speech are known to be associated with prosodic disruptions. However, most algorithms for disfluency detection use only word transcripts. Integrating prosodic cues has proved difficult because of the many sources of variability affecting the acoustic correlates. This paper introduces a new a... | N19-1008 | 10.18653/v1/N19-1008 | null | 1904.04388 | title_snapshot |
N19-1009 | Massively Multilingual Adversarial Speech Recognition | https://aclanthology.org/N19-1009/ | [
"Oliver Adams",
"Matthew Wiesner",
"Shinji Watanabe",
"David Yarowsky"
] | We report on adaptation of multilingual end-to-end speech recognition models trained on as many as 100 languages. Our findings shed light on the relative importance of similarity between the target and pretraining languages along the dimensions of phonetics, phonology, language family, geographical location, and orthog... | N19-1009 | 10.18653/v1/N19-1009 | null | 1904.02210 | title_snapshot |
N19-1010 | Lost in Interpretation: Predicting Untranslated Terminology in Simultaneous Interpretation | https://aclanthology.org/N19-1010/ | [
"Nikolai Vogler",
"Craig Stewart",
"Graham Neubig"
] | Simultaneous interpretation, the translation of speech from one language to another in real-time, is an inherently difficult and strenuous task. One of the greatest challenges faced by interpreters is the accurate translation of difficult terminology like proper names, numbers, or other entities. Intelligent computer-a... | N19-1010 | 10.18653/v1/N19-1010 | null | 1904.00930 | title_snapshot |
N19-1011 | AudioCaps: Generating Captions for Audios in The Wild | https://aclanthology.org/N19-1011/ | [
"Chris Dongjoo Kim",
"Byeongchang Kim",
"Hyunmin Lee",
"Gunhee Kim"
] | We explore the problem of Audio Captioning: generating natural language description for any kind of audio in the wild, which has been surprisingly unexplored in previous research. We contribute a large-scale dataset of 46K audio clips with human-written text pairs collected via crowdsourcing on the AudioSet dataset. Ou... | N19-1011 | 10.18653/v1/N19-1011 | null | null | null |
N19-1012 | “President Vows to Cut <Taxes> Hair”: Dataset and Analysis of Creative Text Editing for Humorous Headlines | https://aclanthology.org/N19-1012/ | [
"Nabil Hossain",
"John Krumm",
"Michael Gamon"
] | We introduce, release, and analyze a new dataset, called Humicroedit, for research in computational humor. Our publicly available data consists of regular English news headlines paired with versions of the same headlines that contain simple replacement edits designed to make them funny. We carefully curated crowdsource... | N19-1012 | 10.18653/v1/N19-1012 | null | 1906.00274 | title_snapshot |
N19-1013 | Answer-based Adversarial Training for Generating Clarification Questions | https://aclanthology.org/N19-1013/ | [
"Sudha Rao",
"Hal Daumé III"
] | We present an approach for generating clarification questions with the goal of eliciting new information that would make the given textual context more complete. We propose that modeling hypothetical answers (to clarification questions) as latent variables can guide our approach into generating more useful clarificatio... | N19-1013 | 10.18653/v1/N19-1013 | null | 1904.02281 | title_snapshot |
N19-1014 | Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data | https://aclanthology.org/N19-1014/ | [
"Wei Zhao",
"Liang Wang",
"Kewei Shen",
"Ruoyu Jia",
"Jingming Liu"
] | Neural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task. In this paper, we propose a copy-augmented architecture for the GEC task by copying the unchanged words from the source sentence to the target sentence. Since the GEC suffers from not having enough la... | N19-1014 | 10.18653/v1/N19-1014 | null | 1903.00138 | title_snapshot |
N19-1015 | Topic-Guided Variational Auto-Encoder for Text Generation | https://aclanthology.org/N19-1015/ | [
"Wenlin Wang",
"Zhe Gan",
"Hongteng Xu",
"Ruiyi Zhang",
"Guoyin Wang",
"Dinghan Shen",
"Changyou Chen",
"Lawrence Carin"
] | We propose a topic-guided variational auto-encoder (TGVAE) model for text generation. Distinct from existing variational auto-encoder (VAE) based approaches, which assume a simple Gaussian prior for latent code, our model specifies the prior as a Gaussian mixture model (GMM) parametrized by a neural topic module. Each ... | N19-1015 | 10.18653/v1/N19-1015 | null | 1903.07137 | title_judge |
N19-1016 | Implementation of a Chomsky-Schützenberger n-best parser for weighted multiple context-free grammars | https://aclanthology.org/N19-1016/ | [
"Thomas Ruprecht",
"Tobias Denkinger"
] | Constituent parsing has been studied extensively in the last decades. Chomsky-Schützenberger parsing as an approach to constituent parsing has only been investigated theoretically, yet. It uses the decomposition of a language into a regular language, a homomorphism, and a bracket language to divide the parsing problem ... | N19-1016 | 10.18653/v1/N19-1016 | null | null | null |
N19-1017 | Phylogenic Multi-Lingual Dependency Parsing | https://aclanthology.org/N19-1017/ | [
"Mathieu Dehouck",
"Pascal Denis"
] | Languages evolve and diverge over time. Their evolutionary history is often depicted in the shape of a phylogenetic tree. Assuming parsing models are representations of their languages grammars, their evolution should follow a structure similar to that of the phylogenetic tree. In this paper, drawing inspiration from m... | N19-1017 | 10.18653/v1/N19-1017 | null | null | null |
N19-1018 | Discontinuous Constituency Parsing with a Stack-Free Transition System and a Dynamic Oracle | https://aclanthology.org/N19-1018/ | [
"Maximin Coavoux",
"Shay B. Cohen"
] | We introduce a novel transition system for discontinuous constituency parsing. Instead of storing subtrees in a stack –i.e. a data structure with linear-time sequential access– the proposed system uses a set of parsing items, with constant-time random access. This change makes it possible to construct any discontinuous... | N19-1018 | 10.18653/v1/N19-1018 | null | 1904.00615 | title_snapshot |
N19-1019 | How Bad are PoS Tagger in Cross-Corpora Settings? Evaluating Annotation Divergence in the UD Project. | https://aclanthology.org/N19-1019/ | [
"Guillaume Wisniewski",
"François Yvon"
] | The performance of Part-of-Speech tagging varies significantly across the treebanks of the Universal Dependencies project. This work points out that these variations may result from divergences between the annotation of train and test sets. We show how the annotation variation principle, introduced by Dickinson and Meu... | N19-1019 | 10.18653/v1/N19-1019 | null | null | null |
N19-1020 | CCG Parsing Algorithm with Incremental Tree Rotation | https://aclanthology.org/N19-1020/ | [
"Miloš Stanojević",
"Mark Steedman"
] | The main obstacle to incremental sentence processing arises from right-branching constituent structures, which are present in the majority of English sentences, as well as optional constituents that adjoin on the right, such as right adjuncts and right conjuncts. In CCG, many right-branching derivations can be replaced... | N19-1020 | 10.18653/v1/N19-1020 | null | null | null |
N19-1021 | Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing | https://aclanthology.org/N19-1021/ | [
"Hao Fu",
"Chunyuan Li",
"Xiaodong Liu",
"Jianfeng Gao",
"Asli Celikyilmaz",
"Lawrence Carin"
] | Variational autoencoders (VAE) with an auto-regressive decoder have been applied for many natural language processing (NLP) tasks. VAE objective consists of two terms, the KL regularization term and the reconstruction term, balanced by a weighting hyper-parameter \beta. One notorious training difficulty is that the KL ... | N19-1021 | 10.18653/v1/N19-1021 | null | 1903.10145 | title_snapshot |
N19-1022 | Recurrent models and lower bounds for projective syntactic decoding | https://aclanthology.org/N19-1022/ | [
"Natalie Schluter"
] | The current state-of-the-art in neural graph-based parsing uses only approximate decoding at the training phase. In this paper aim to understand this result better. We show how recurrent models can carry out projective maximum spanning tree decoding. This result holds for both current state-of-the-art models for shift-... | N19-1022 | 10.18653/v1/N19-1022 | null | null | null |
N19-1023 | Evaluating Composition Models for Verb Phrase Elliptical Sentence Embeddings | https://aclanthology.org/N19-1023/ | [
"Gijs Wijnholds",
"Mehrnoosh Sadrzadeh"
] | Ellipsis is a natural language phenomenon where part of a sentence is missing and its information must be recovered from its surrounding context, as in “Cats chase dogs and so do foxes.”. Formal semantics has different methods for resolving ellipsis and recovering the missing information, but the problem has not been c... | N19-1023 | 10.18653/v1/N19-1023 | null | null | null |
N19-1024 | Neural Finite-State Transducers: Beyond Rational Relations | https://aclanthology.org/N19-1024/ | [
"Chu-Cheng Lin",
"Hao Zhu",
"Matthew R. Gormley",
"Jason Eisner"
] | We introduce neural finite state transducers (NFSTs), a family of string transduction models defining joint and conditional probability distributions over pairs of strings. The probability of a string pair is obtained by marginalizing over all its accepting paths in a finite state transducer. In contrast to ordinary we... | N19-1024 | 10.18653/v1/N19-1024 | null | null | null |
N19-1025 | Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling | https://aclanthology.org/N19-1025/ | [
"Prince Zizhuang Wang",
"William Yang Wang"
] | Recurrent Variational Autoencoder has been widely used for language modeling and text generation tasks. These models often face a difficult optimization problem, also known as KL vanishing, where the posterior easily collapses to the prior and model will ignore latent codes in generative tasks. To address this problem,... | N19-1025 | 10.18653/v1/N19-1025 | null | 1904.02399 | title_snapshot |
N19-1026 | A Study of Incorrect Paraphrases in Crowdsourced User Utterances | https://aclanthology.org/N19-1026/ | [
"Mohammad-Ali Yaghoub-Zadeh-Fard",
"Boualem Benatallah",
"Moshe Chai Barukh",
"Shayan Zamanirad"
] | Developing bots demands highquality training samples, typically in the form of user utterances and their associated intents. Given the fuzzy nature of human language, such datasets ideally must cover all possible utterances of each single intent. Crowdsourcing has widely been used to collect such inclusive datasets by ... | N19-1026 | 10.18653/v1/N19-1026 | null | null | null |
N19-1027 | ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters | https://aclanthology.org/N19-1027/ | [
"Abdalghani Abujabal",
"Rishiraj Saha Roy",
"Mohamed Yahya",
"Gerhard Weikum"
] | To bridge the gap between the capabilities of the state-of-the-art in factoid question answering (QA) and what users ask, we need large datasets of real user questions that capture the various question phenomena users are interested in, and the diverse ways in which these questions are formulated. We introduce ComQA, a... | N19-1027 | 10.18653/v1/N19-1027 | null | 1809.09528 | title_snapshot |
N19-1028 | FreebaseQA: A New Factoid QA Data Set Matching Trivia-Style Question-Answer Pairs with Freebase | https://aclanthology.org/N19-1028/ | [
"Kelvin Jiang",
"Dekun Wu",
"Hui Jiang"
] | In this paper, we present a new data set, named FreebaseQA, for open-domain factoid question answering (QA) tasks over structured knowledge bases, like Freebase. The data set is generated by matching trivia-type question-answer pairs with subject-predicate-object triples in Freebase. For each collected question-answer ... | N19-1028 | 10.18653/v1/N19-1028 | null | null | null |
N19-1029 | Simple Question Answering with Subgraph Ranking and Joint-Scoring | https://aclanthology.org/N19-1029/ | [
"Wenbo Zhao",
"Tagyoung Chung",
"Anuj Goyal",
"Angeliki Metallinou"
] | Knowledge graph based simple question answering (KBSQA) is a major area of research within question answering. Although only dealing with simple questions, i.e., questions that can be answered through a single knowledge base (KB) fact, this task is neither simple nor close to being solved. Targeting on the two main ste... | N19-1029 | 10.18653/v1/N19-1029 | null | 1904.04049 | title_snapshot |
N19-1030 | Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering | https://aclanthology.org/N19-1030/ | [
"Jianmo Ni",
"Chenguang Zhu",
"Weizhu Chen",
"Julian McAuley"
] | Open-domain question answering remains a challenging task as it requires models that are capable of understanding questions and answers, collecting useful information, and reasoning over evidence. Previous work typically formulates this task as a reading comprehension or entailment problem given evidence retrieved from... | N19-1030 | 10.18653/v1/N19-1030 | null | 1808.09492 | title_snapshot |
N19-1031 | UHop: An Unrestricted-Hop Relation Extraction Framework for Knowledge-Based Question Answering | https://aclanthology.org/N19-1031/ | [
"Zi-Yuan Chen",
"Chih-Hung Chang",
"Yi-Pei Chen",
"Jijnasa Nayak",
"Lun-Wei Ku"
] | In relation extraction for knowledge-based question answering, searching from one entity to another entity via a single relation is called “one hop”. In related work, an exhaustive search from all one-hop relations, two-hop relations, and so on to the max-hop relations in the knowledge graph is necessary but expensive.... | N19-1031 | 10.18653/v1/N19-1031 | null | 1904.01246 | title_snapshot |
N19-1032 | BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering | https://aclanthology.org/N19-1032/ | [
"Yu Cao",
"Meng Fang",
"Dacheng Tao"
] | Multi-hop reasoning question answering requires deep comprehension of relationships between various documents and queries. We propose a Bi-directional Attention Entity Graph Convolutional Network (BAG), leveraging relationships between nodes in an entity graph and attention information between a query and the entity gr... | N19-1032 | 10.18653/v1/N19-1032 | null | 1904.04969 | title_snapshot |
N19-1033 | Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation | https://aclanthology.org/N19-1033/ | [
"Radu Tudor Ionescu",
"Andrei Butnaru"
] | In this paper, we propose a novel representation for text documents based on aggregating word embedding vectors into document embeddings. Our approach is inspired by the Vector of Locally-Aggregated Descriptors used for image representation, and it works as follows. First, the word embeddings gathered from a collection... | N19-1033 | 10.18653/v1/N19-1033 | null | 1902.08850 | title_snapshot |
N19-1034 | Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis | https://aclanthology.org/N19-1034/ | [
"Md Shad Akhtar",
"Dushyant Chauhan",
"Deepanway Ghosal",
"Soujanya Poria",
"Asif Ekbal",
"Pushpak Bhattacharyya"
] | Related tasks often have inter-dependence on each other and perform better when solved in a joint framework. In this paper, we present a deep multi-task learning framework that jointly performs sentiment and emotion analysis both. The multi-modal inputs (i.e. text, acoustic and visual frames) of a video convey diverse ... | N19-1034 | 10.18653/v1/N19-1034 | null | 1905.05812 | title_snapshot |
N19-1035 | Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence | https://aclanthology.org/N19-1035/ | [
"Chi Sun",
"Luyao Huang",
"Xipeng Qiu"
] | Aspect-based sentiment analysis (ABSA), which aims to identify fine-grained opinion polarity towards a specific aspect, is a challenging subtask of sentiment analysis (SA). In this paper, we construct an auxiliary sentence from the aspect and convert ABSA to a sentence-pair classification task, such as question answeri... | N19-1035 | 10.18653/v1/N19-1035 | null | 1903.09588 | title_snapshot |
N19-1036 | A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification | https://aclanthology.org/N19-1036/ | [
"Ziqian Zeng",
"Wenxuan Zhou",
"Xin Liu",
"Yangqiu Song"
] | In this paper, we propose a variational approach to weakly supervised document-level multi-aspect sentiment classification. Instead of using user-generated ratings or annotations provided by domain experts, we use target-opinion word pairs as “supervision.” These word pairs can be extracted by using dependency parsers ... | N19-1036 | 10.18653/v1/N19-1036 | null | 1904.05055 | title_snapshot |
N19-1037 | HiGRU: Hierarchical Gated Recurrent Units for Utterance-Level Emotion Recognition | https://aclanthology.org/N19-1037/ | [
"Wenxiang Jiao",
"Haiqin Yang",
"Irwin King",
"Michael R. Lyu"
] | In this paper, we address three challenges in utterance-level emotion recognition in dialogue systems: (1) the same word can deliver different emotions in different contexts; (2) some emotions are rarely seen in general dialogues; (3) long-range contextual information is hard to be effectively captured. We therefore pr... | N19-1037 | 10.18653/v1/N19-1037 | null | 1904.04446 | title_snapshot |
N19-1038 | Learning Interpretable Negation Rules via Weak Supervision at Document Level: A Reinforcement Learning Approach | https://aclanthology.org/N19-1038/ | [
"Nicolas Pröllochs",
"Stefan Feuerriegel",
"Dirk Neumann"
] | Negation scope detection is widely performed as a supervised learning task which relies upon negation labels at word level. This suffers from two key drawbacks: (1) such granular annotations are costly and (2) highly subjective, since, due to the absence of explicit linguistic resolution rules, human annotators often d... | N19-1038 | 10.18653/v1/N19-1038 | null | null | null |
N19-1039 | Simplified Neural Unsupervised Domain Adaptation | https://aclanthology.org/N19-1039/ | [
"Timothy Miller"
] | Unsupervised domain adaptation (UDA) is the task of training a statistical model on labeled data from a source domain to achieve better performance on data from a target domain, with access to only unlabeled data in the target domain. Existing state-of-the-art UDA approaches use neural networks to learn representations... | N19-1039 | 10.18653/v1/N19-1039 | null | 1905.09153 | title_snapshot |
N19-1040 | Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision | https://aclanthology.org/N19-1040/ | [
"Yanlin Feng",
"Xiaojun Wan"
] | Word embeddings learned in two languages can be mapped to a common space to produce Bilingual Word Embeddings (BWE). Unsupervised BWE methods learn such a mapping without any parallel data. However, these methods are mainly evaluated on tasks of word translation or word similarity. We show that these methods fail to ca... | N19-1040 | 10.18653/v1/N19-1040 | null | null | null |
N19-1041 | ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems | https://aclanthology.org/N19-1041/ | [
"Inigo Jauregi Unanue",
"Ehsan Zare Borzeshi",
"Nazanin Esmaili",
"Massimo Piccardi"
] | Regularization of neural machine translation is still a significant problem, especially in low-resource settings. To mollify this problem, we propose regressing word embeddings (ReWE) as a new regularization technique in a system that is jointly trained to predict the next word in the translation (categorical value) an... | N19-1041 | 10.18653/v1/N19-1041 | null | 1904.02461 | title_snapshot |
N19-1042 | Lost in Machine Translation: A Method to Reduce Meaning Loss | https://aclanthology.org/N19-1042/ | [
"Reuben Cohn-Gordon",
"Noah Goodman"
] | A desideratum of high-quality translation systems is that they preserve meaning, in the sense that two sentences with different meanings should not translate to one and the same sentence in another language. However, state-of-the-art systems often fail in this regard, particularly in cases where the source and target l... | N19-1042 | 10.18653/v1/N19-1042 | null | 1902.09514 | title_snapshot |
N19-1043 | Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation | https://aclanthology.org/N19-1043/ | [
"Xing Niu",
"Weijia Xu",
"Marine Carpuat"
] | We aim to better exploit the limited amounts of parallel text available in low-resource settings by introducing a differentiable reconstruction loss for neural machine translation (NMT). This loss compares original inputs to reconstructed inputs, obtained by back-translating translation hypotheses into the input langua... | N19-1043 | 10.18653/v1/N19-1043 | null | 1811.01116 | title_snapshot |
N19-1044 | Code-Switching for Enhancing NMT with Pre-Specified Translation | https://aclanthology.org/N19-1044/ | [
"Kai Song",
"Yue Zhang",
"Heng Yu",
"Weihua Luo",
"Kun Wang",
"Min Zhang"
] | Leveraging user-provided translation to constrain NMT has practical significance. Existing methods can be classified into two main categories, namely the use of placeholder tags for lexicon words and the use of hard constraints during decoding. Both methods can hurt translation fidelity for various reasons. We investig... | N19-1044 | 10.18653/v1/N19-1044 | null | 1904.09107 | title_snapshot |
N19-1045 | Aligning Vector-spaces with Noisy Supervised Lexicon | https://aclanthology.org/N19-1045/ | [
"Noa Yehezkel Lubin",
"Jacob Goldberger",
"Yoav Goldberg"
] | The problem of learning to translate between two vector spaces given a set of aligned points arises in several application areas of NLP. Current solutions assume that the lexicon which defines the alignment pairs is noise-free. We consider the case where the set of aligned points is allowed to contain an amount of nois... | N19-1045 | 10.18653/v1/N19-1045 | null | 1903.10238 | title_judge |
N19-1046 | Understanding and Improving Hidden Representations for Neural Machine Translation | https://aclanthology.org/N19-1046/ | [
"Guanlin Li",
"Lemao Liu",
"Xintong Li",
"Conghui Zhu",
"Tiejun Zhao",
"Shuming Shi"
] | Multilayer architectures are currently the gold standard for large-scale neural machine translation. Existing works have explored some methods for understanding the hidden representations, however, they have not sought to improve the translation quality rationally according to their understanding. Towards understanding... | N19-1046 | 10.18653/v1/N19-1046 | null | null | null |
N19-1047 | Content Differences in Syntactic and Semantic Representation | https://aclanthology.org/N19-1047/ | [
"Daniel Hershcovich",
"Omri Abend",
"Ari Rappoport"
] | Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate. The debate has been constrained by the scarcity of empirical comparative studies between syntactic and semantic schemes, which hinders the development of parsing methods informed by the details... | N19-1047 | 10.18653/v1/N19-1047 | null | 1903.06494 | title_judge |
N19-1048 | Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts | https://aclanthology.org/N19-1048/ | [
"Timo Schick",
"Hinrich Schütze"
] | Learning high-quality embeddings for rare words is a hard problem because of sparse context information. Mimicking (Pinter et al., 2017) has been proposed as a solution: given embeddings learned by a standard algorithm, a model is first trained to reproduce embeddings of frequent words from their surface form and then ... | N19-1048 | 10.18653/v1/N19-1048 | null | 1904.01617 | title_snapshot |
N19-1049 | Evaluating Style Transfer for Text | https://aclanthology.org/N19-1049/ | [
"Remi Mir",
"Bjarke Felbo",
"Nick Obradovich",
"Iyad Rahwan"
] | Research in the area of style transfer for text is currently bottlenecked by a lack of standard evaluation practices. This paper aims to alleviate this issue by experimentally identifying best practices with a Yelp sentiment dataset. We specify three aspects of interest (style transfer intensity, content preservation, ... | N19-1049 | 10.18653/v1/N19-1049 | null | 1904.02295 | title_snapshot |
N19-1050 | Big BiRD: A Large, Fine-Grained, Bigram Relatedness Dataset for Examining Semantic Composition | https://aclanthology.org/N19-1050/ | [
"Shima Asaadi",
"Saif Mohammad",
"Svetlana Kiritchenko"
] | Bigrams (two-word sequences) hold a special place in semantic composition research since they are the smallest unit formed by composing words. A semantic relatedness dataset that includes bigrams will thus be useful in the development of automatic methods of semantic composition. However, existing relatedness datasets ... | N19-1050 | 10.18653/v1/N19-1050 | null | null | null |
N19-1051 | Outlier Detection for Improved Data Quality and Diversity in Dialog Systems | https://aclanthology.org/N19-1051/ | [
"Stefan Larson",
"Anish Mahendran",
"Andrew Lee",
"Jonathan K. Kummerfeld",
"Parker Hill",
"Michael A. Laurenzano",
"Johann Hauswald",
"Lingjia Tang",
"Jason Mars"
] | In a corpus of data, outliers are either errors: mistakes in the data that are counterproductive, or are unique: informative samples that improve model robustness. Identifying outliers can lead to better datasets by (1) removing noise in datasets and (2) guiding collection of additional data to fill gaps. However, the ... | N19-1051 | 10.18653/v1/N19-1051 | null | 1904.03122 | title_snapshot |
N19-1052 | Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives | https://aclanthology.org/N19-1052/ | [
"Liye Fu",
"Jonathan P. Chang",
"Cristian Danescu-Niculescu-Mizil"
] | People often share personal narratives in order to seek advice from others. To properly infer the narrator’s intention, one needs to apply a certain degree of common sense and social intuition. To test the capabilities of NLP systems to recover such intuition, we introduce the new task of inferring what is the advice-s... | N19-1052 | 10.18653/v1/N19-1052 | null | 1904.01587 | title_snapshot |
N19-1053 | Seeing Things from a Different Angle:Discovering Diverse Perspectives about Claims | https://aclanthology.org/N19-1053/ | [
"Sihao Chen",
"Daniel Khashabi",
"Wenpeng Yin",
"Chris Callison-Burch",
"Dan Roth"
] | One key consequence of the information revolution is a significant increase and a contamination of our information supply. The practice of fact checking won’t suffice to eliminate the biases in text data we observe, as the degree of factuality alone does not determine whether biases exist in the spectrum of opinions vi... | N19-1053 | 10.18653/v1/N19-1053 | null | 1906.03538 | title_snapshot |
N19-1054 | IMHO Fine-Tuning Improves Claim Detection | https://aclanthology.org/N19-1054/ | [
"Tuhin Chakrabarty",
"Christopher Hidey",
"Kathy McKeown"
] | Claims are the central component of an argument. Detecting claims across different domains or data sets can often be challenging due to their varying conceptualization. We propose to alleviate this problem by fine-tuning a language model using a Reddit corpus of 5.5 million opinionated claims. These claims are self-lab... | N19-1054 | 10.18653/v1/N19-1054 | null | 1905.07000 | title_snapshot |
N19-1055 | Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog | https://aclanthology.org/N19-1055/ | [
"Rashmi Gangadharaiah",
"Balakrishnan Narayanaswamy"
] | Neural network models have recently gained traction for sentence-level intent classification and token-based slot-label identification. In many real-world scenarios, users have multiple intents in the same utterance, and a token-level slot label can belong to more than one intent. We investigate an attention-based neur... | N19-1055 | 10.18653/v1/N19-1055 | null | null | null |
N19-1056 | CITE: A Corpus of Image-Text Discourse Relations | https://aclanthology.org/N19-1056/ | [
"Malihe Alikhani",
"Sreyasi Nag Chowdhury",
"Gerard de Melo",
"Matthew Stone"
] | This paper presents a novel crowd-sourced resource for multimodal discourse: our resource characterizes inferences in image-text contexts in the domain of cooking recipes in the form of coherence relations. Like previous corpora annotating discourse structure between text arguments, such as the Penn Discourse Treebank,... | N19-1056 | 10.18653/v1/N19-1056 | null | 1904.06286 | title_snapshot |
N19-1057 | Improving Dialogue State Tracking by Discerning the Relevant Context | https://aclanthology.org/N19-1057/ | [
"Sanuj Sharma",
"Prafulla Kumar Choubey",
"Ruihong Huang"
] | A typical conversation comprises of multiple turns between participants where they go back and forth between different topics. At each user turn, dialogue state tracking (DST) aims to estimate user’s goal by processing the current utterance. However, in many turns, users implicitly refer to the previous goal, necessita... | N19-1057 | 10.18653/v1/N19-1057 | null | 1904.02800 | title_snapshot |
N19-1058 | CLEVR-Dialog: A Diagnostic Dataset for Multi-Round Reasoning in Visual Dialog | https://aclanthology.org/N19-1058/ | [
"Satwik Kottur",
"José M. F. Moura",
"Devi Parikh",
"Dhruv Batra",
"Marcus Rohrbach"
] | Visual Dialog is a multimodal task of answering a sequence of questions grounded in an image (using the conversation history as context). It entails challenges in vision, language, reasoning, and grounding. However, studying these subtasks in isolation on large, real datasets is infeasible as it requires prohibitively-... | N19-1058 | 10.18653/v1/N19-1058 | null | 1903.03166 | title_snapshot |
N19-1059 | Learning Outside the Box: Discourse-level Features Improve Metaphor Identification | https://aclanthology.org/N19-1059/ | [
"Jesse Mu",
"Helen Yannakoudakis",
"Ekaterina Shutova"
] | Most current approaches to metaphor identification use restricted linguistic contexts, e.g. by considering only a verb’s arguments or the sentence containing a phrase. Inspired by pragmatic accounts of metaphor, we argue that broader discourse features are crucial for better metaphor identification. We train simple gra... | N19-1059 | 10.18653/v1/N19-1059 | null | 1904.02246 | title_snapshot |
N19-1060 | Detection of Abusive Language: the Problem of Biased Datasets | https://aclanthology.org/N19-1060/ | [
"Michael Wiegand",
"Josef Ruppenhofer",
"Thomas Kleinbauer"
] | We discuss the impact of data bias on abusive language detection. We show that classification scores on popular datasets reported in previous work are much lower under realistic settings in which this bias is reduced. Such biases are most notably observed on datasets that are created by focused sampling instead of rand... | N19-1060 | 10.18653/v1/N19-1060 | null | null | null |
N19-1061 | Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them | https://aclanthology.org/N19-1061/ | [
"Hila Gonen",
"Yoav Goldberg"
] | Word embeddings are widely used in NLP for a vast range of tasks. It was shown that word embeddings derived from text corpora reflect gender biases in society. This phenomenon is pervasive and consistent across different word embedding models, causing serious concern. Several recent works tackle this problem, and propo... | N19-1061 | 10.18653/v1/N19-1061 | null | 1903.03862 | title_snapshot |
N19-1062 | Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings | https://aclanthology.org/N19-1062/ | [
"Thomas Manzini",
"Lim Yao Chong",
"Alan W Black",
"Yulia Tsvetkov"
] | Online texts - across genres, registers, domains, and styles - are riddled with human stereotypes, expressed in overt or subtle ways. Word embeddings, trained on these texts, perpetuate and amplify these stereotypes, and propagate biases to machine learning models that use word embeddings as features. In this work, we ... | N19-1062 | 10.18653/v1/N19-1062 | null | 1904.04047 | title_snapshot |
N19-1063 | On Measuring Social Biases in Sentence Encoders | https://aclanthology.org/N19-1063/ | [
"Chandler May",
"Alex Wang",
"Shikha Bordia",
"Samuel R. Bowman",
"Rachel Rudinger"
] | The Word Embedding Association Test shows that GloVe and word2vec word embeddings exhibit human-like implicit biases based on gender, race, and other social constructs (Caliskan et al., 2017). Meanwhile, research on learning reusable text representations has begun to explore sentence-level texts, with some sentence enc... | N19-1063 | 10.18653/v1/N19-1063 | null | 1903.10561 | title_snapshot |
N19-1064 | Gender Bias in Contextualized Word Embeddings | https://aclanthology.org/N19-1064/ | [
"Jieyu Zhao",
"Tianlu Wang",
"Mark Yatskar",
"Ryan Cotterell",
"Vicente Ordonez",
"Kai-Wei Chang"
] | In this paper, we quantify, analyze and mitigate gender bias exhibited in ELMo’s contextualized word vectors. First, we conduct several intrinsic analyses and find that (1) training data for ELMo contains significantly more male than female entities, (2) the trained ELMo embeddings systematically encode gender informat... | N19-1064 | 10.18653/v1/N19-1064 | null | 1904.03310 | title_snapshot |
N19-1065 | Combining Sentiment Lexica with a Multi-View Variational Autoencoder | https://aclanthology.org/N19-1065/ | [
"Alexander Hoyle",
"Lawrence Wolf-Sonkin",
"Hanna Wallach",
"Ryan Cotterell",
"Isabelle Augenstein"
] | When assigning quantitative labels to a dataset, different methodologies may rely on different scales. In particular, when assigning polarities to words in a sentiment lexicon, annotators may use binary, categorical, or continuous labels. Naturally, it is of interest to unify these labels from disparate scales to both ... | N19-1065 | 10.18653/v1/N19-1065 | null | 1904.02839 | title_snapshot |
N19-1066 | Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role Labeling | https://aclanthology.org/N19-1066/ | [
"Meishan Zhang",
"Peili Liang",
"Guohong Fu"
] | Opinion role labeling (ORL) is an important task for fine-grained opinion mining, which identifies important opinion arguments such as holder and target for a given opinion trigger. The task is highly correlative with semantic role labeling (SRL), which identifies important semantic arguments such as agent and patient ... | N19-1066 | 10.18653/v1/N19-1066 | null | null | null |
N19-1067 | Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters | https://aclanthology.org/N19-1067/ | [
"Evgeny Kim",
"Roman Klinger"
] | The development of a fictional plot is centered around characters who closely interact with each other forming dynamic social networks. In literature analysis, such networks have mostly been analyzed without particular relation types or focusing on roles which the characters take with respect to each other. We argue th... | N19-1067 | 10.18653/v1/N19-1067 | null | 1903.12453 | title_snapshot |
N19-1068 | Generalizing Unmasking for Short Texts | https://aclanthology.org/N19-1068/ | [
"Janek Bevendorff",
"Benno Stein",
"Matthias Hagen",
"Martin Potthast"
] | Authorship verification is the problem of inferring whether two texts were written by the same author. For this task, unmasking is one of the most robust approaches as of today with the major shortcoming of only being applicable to book-length texts. In this paper, we present a generalized unmasking approach which allo... | N19-1068 | 10.18653/v1/N19-1068 | null | null | null |
N19-1069 | Adversarial Training for Satire Detection: Controlling for Confounding Variables | https://aclanthology.org/N19-1069/ | [
"Robert McHardy",
"Heike Adel",
"Roman Klinger"
] | The automatic detection of satire vs. regular news is relevant for downstream applications (for instance, knowledge base population) and to improve the understanding of linguistic characteristics of satire. Recent approaches build upon corpora which have been labeled automatically based on article sources. We hypothesi... | N19-1069 | 10.18653/v1/N19-1069 | null | 1902.11145 | title_snapshot |
N19-1070 | Keyphrase Generation: A Text Summarization Struggle | https://aclanthology.org/N19-1070/ | [
"Erion Çano",
"Ondřej Bojar"
] | Authors’ keyphrases assigned to scientific articles are essential for recognizing content and topic aspects. Most of the proposed supervised and unsupervised methods for keyphrase generation are unable to produce terms that are valuable but do not appear in the text. In this paper, we explore the possibility of conside... | N19-1070 | 10.18653/v1/N19-1070 | null | 1904.00110 | title_snapshot |
N19-1071 | SEQˆ3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression | https://aclanthology.org/N19-1071/ | [
"Christos Baziotis",
"Ion Androutsopoulos",
"Ioannis Konstas",
"Alexandros Potamianos"
] | Neural sequence-to-sequence models are currently the dominant approach in several natural language processing tasks, but require large parallel corpora. We present a sequence-to-sequence-to-sequence autoencoder (SEQˆ3), consisting of two chained encoder-decoder pairs, with words used as a sequence of discrete latent va... | N19-1071 | 10.18653/v1/N19-1071 | null | 1904.03651 | title_snapshot |
N19-1072 | Crowdsourcing Lightweight Pyramids for Manual Summary Evaluation | https://aclanthology.org/N19-1072/ | [
"Ori Shapira",
"David Gabay",
"Yang Gao",
"Hadar Ronen",
"Ramakanth Pasunuru",
"Mohit Bansal",
"Yael Amsterdamer",
"Ido Dagan"
] | Conducting a manual evaluation is considered an essential part of summary evaluation methodology. Traditionally, the Pyramid protocol, which exhaustively compares system summaries to references, has been perceived as very reliable, providing objective scores. Yet, due to the high cost of the Pyramid method and the requ... | N19-1072 | 10.18653/v1/N19-1072 | null | 1904.05929 | title_snapshot |
N19-1073 | Serial Recall Effects in Neural Language Modeling | https://aclanthology.org/N19-1073/ | [
"Hassan Hajipoor",
"Hadi Amiri",
"Maseud Rahgozar",
"Farhad Oroumchian"
] | Serial recall experiments study the ability of humans to recall words in the order in which they occurred. The following serial recall effects are generally investigated in studies with humans: word length and frequency, primacy and recency, semantic confusion, repetition, and transposition effects. In this research, w... | N19-1073 | 10.18653/v1/N19-1073 | null | null | null |
N19-1074 | Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization | https://aclanthology.org/N19-1074/ | [
"Tobias Falke",
"Iryna Gurevych"
] | Concept map-based multi-document summarization has recently been proposed as a variant of the traditional summarization task with graph-structured summaries. As shown by previous work, the grouping of coreferent concept mentions across documents is a crucial subtask of it. However, while the current state-of-the-art me... | N19-1074 | 10.18653/v1/N19-1074 | null | null | null |
N19-1075 | Syntax-aware Neural Semantic Role Labeling with Supertags | https://aclanthology.org/N19-1075/ | [
"Jungo Kasai",
"Dan Friedman",
"Robert Frank",
"Dragomir Radev",
"Owen Rambow"
] | We introduce a new syntax-aware model for dependency-based semantic role labeling that outperforms syntax-agnostic models for English and Spanish. We use a BiLSTM to tag the text with supertags extracted from dependency parses, and we feed these supertags, along with words and parts of speech, into a deep highway BiLST... | N19-1075 | 10.18653/v1/N19-1075 | null | 1903.05260 | title_snapshot |
N19-1076 | Left-to-Right Dependency Parsing with Pointer Networks | https://aclanthology.org/N19-1076/ | [
"Daniel Fernández-González",
"Carlos Gómez-Rodríguez"
] | We propose a novel transition-based algorithm that straightforwardly parses sentences from left to right by building n attachments, with n being the length of the input sentence. Similarly to the recent stack-pointer parser by Ma et al. (2018), we use the pointer network framework that, given a word, can directly point... | N19-1076 | 10.18653/v1/N19-1076 | null | 1903.08445 | title_snapshot |
N19-1077 | Viable Dependency Parsing as Sequence Labeling | https://aclanthology.org/N19-1077/ | [
"Michalina Strzyz",
"David Vilares",
"Carlos Gómez-Rodríguez"
] | We recast dependency parsing as a sequence labeling problem, exploring several encodings of dependency trees as labels. While dependency parsing by means of sequence labeling had been attempted in existing work, results suggested that the technique was impractical. We show instead that with a conventional BILSTM-based ... | N19-1077 | 10.18653/v1/N19-1077 | null | 1902.10505 | title_snapshot |
N19-1078 | Pooled Contextualized Embeddings for Named Entity Recognition | https://aclanthology.org/N19-1078/ | [
"Alan Akbik",
"Tanja Bergmann",
"Roland Vollgraf"
] | Contextual string embeddings are a recent type of contextualized word embedding that were shown to yield state-of-the-art results when utilized in a range of sequence labeling tasks. They are based on character-level language models which treat text as distributions over characters and are capable of generating embeddi... | N19-1078 | 10.18653/v1/N19-1078 | null | null | null |
N19-1079 | Better Modeling of Incomplete Annotations for Named Entity Recognition | https://aclanthology.org/N19-1079/ | [
"Zhanming Jie",
"Pengjun Xie",
"Wei Lu",
"Ruixue Ding",
"Linlin Li"
] | Supervised approaches to named entity recognition (NER) are largely developed based on the assumption that the training data is fully annotated with named entity information. However, in practice, annotated data can often be imperfect with one typical issue being the training data may contain incomplete annotations. We... | N19-1079 | 10.18653/v1/N19-1079 | null | null | null |
N19-1080 | Event Detection without Triggers | https://aclanthology.org/N19-1080/ | [
"Shulin Liu",
"Yang Li",
"Feng Zhang",
"Tao Yang",
"Xinpeng Zhou"
] | The goal of event detection (ED) is to detect the occurrences of events and categorize them. Previous work solved this task by recognizing and classifying event triggers, which is defined as the word or phrase that most clearly expresses an event occurrence. As a consequence, existing approaches required both annotated... | N19-1080 | 10.18653/v1/N19-1080 | null | null | null |
N19-1081 | Sub-event detection from twitter streams as a sequence labeling problem | https://aclanthology.org/N19-1081/ | [
"Giannis Bekoulis",
"Johannes Deleu",
"Thomas Demeester",
"Chris Develder"
] | This paper introduces improved methods for sub-event detection in social media streams, by applying neural sequence models not only on the level of individual posts, but also directly on the stream level. Current approaches to identify sub-events within a given event, such as a goal during a soccer match, essentially d... | N19-1081 | 10.18653/v1/N19-1081 | null | 1903.05396 | title_snapshot |
N19-1082 | GraphIE: A Graph-Based Framework for Information Extraction | https://aclanthology.org/N19-1082/ | [
"Yujie Qian",
"Enrico Santus",
"Zhijing Jin",
"Jiang Guo",
"Regina Barzilay"
] | Most modern Information Extraction (IE) systems are implemented as sequential taggers and only model local dependencies. Non-local and non-sequential context is, however, a valuable source of information to improve predictions. In this paper, we introduce GraphIE, a framework that operates over a graph representing a b... | N19-1082 | 10.18653/v1/N19-1082 | null | 1810.13083 | title_snapshot |
N19-1083 | OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference | https://aclanthology.org/N19-1083/ | [
"Dongxu Zhang",
"Subhabrata Mukherjee",
"Colin Lockard",
"Luna Dong",
"Andrew McCallum"
] | In this paper, we consider advancing web-scale knowledge extraction and alignment by integrating OpenIE extractions in the form of (subject, predicate, object) triples with Knowledge Bases (KB). Traditional techniques from universal schema and from schema mapping fall in two extremes: either they perform instance-level... | N19-1083 | 10.18653/v1/N19-1083 | null | 1904.12606 | title_snapshot |
N19-1084 | Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing | https://aclanthology.org/N19-1084/ | [
"Wenhan Xiong",
"Jiawei Wu",
"Deren Lei",
"Mo Yu",
"Shiyu Chang",
"Xiaoxiao Guo",
"William Yang Wang"
] | Existing entity typing systems usually exploit the type hierarchy provided by knowledge base (KB) schema to model label correlations and thus improve the overall performance. Such techniques, however, are not directly applicable to more open and practical scenarios where the type set is not restricted by KB schema and ... | N19-1084 | 10.18653/v1/N19-1084 | null | 1903.02591 | title_snapshot |
N19-1085 | Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data | https://aclanthology.org/N19-1085/ | [
"Yin Jou Huang",
"Jing Lu",
"Sadao Kurohashi",
"Vincent Ng"
] | Argument compatibility is a linguistic condition that is frequently incorporated into modern event coreference resolution systems. If two event mentions have incompatible arguments in any of the argument roles, they cannot be coreferent. On the other hand, if these mentions have compatible arguments, then this may be u... | N19-1085 | 10.18653/v1/N19-1085 | null | null | null |
N19-1086 | Sentence Embedding Alignment for Lifelong Relation Extraction | https://aclanthology.org/N19-1086/ | [
"Hong Wang",
"Wenhan Xiong",
"Mo Yu",
"Xiaoxiao Guo",
"Shiyu Chang",
"William Yang Wang"
] | Conventional approaches to relation extraction usually require a fixed set of pre-defined relations. Such requirement is hard to meet in many real applications, especially when new data and relations are emerging incessantly and it is computationally expensive to store all data and re-train the whole model every time n... | N19-1086 | 10.18653/v1/N19-1086 | null | 1903.02588 | title_snapshot |
N19-1087 | Description-Based Zero-shot Fine-Grained Entity Typing | https://aclanthology.org/N19-1087/ | [
"Rasha Obeidat",
"Xiaoli Fern",
"Hamed Shahbazi",
"Prasad Tadepalli"
] | Fine-grained Entity typing (FGET) is the task of assigning a fine-grained type from a hierarchy to entity mentions in the text. As the taxonomy of types evolves continuously, it is desirable for an entity typing system to be able to recognize novel types without additional training. This work proposes a zero-shot entit... | N19-1087 | 10.18653/v1/N19-1087 | null | null | null |
N19-1088 | Adversarial Decomposition of Text Representation | https://aclanthology.org/N19-1088/ | [
"Alexey Romanov",
"Anna Rumshisky",
"Anna Rogers",
"David Donahue"
] | In this paper, we present a method for adversarial decomposition of text representation. This method can be used to decompose a representation of an input sentence into several independent vectors, each of them responsible for a specific aspect of the input sentence. We evaluate the proposed method on two case studies:... | N19-1088 | 10.18653/v1/N19-1088 | null | 1808.09042 | title_snapshot |
N19-1089 | PoMo: Generating Entity-Specific Post-Modifiers in Context | https://aclanthology.org/N19-1089/ | [
"Jun Seok Kang",
"Robert Logan",
"Zewei Chu",
"Yang Chen",
"Dheeru Dua",
"Kevin Gimpel",
"Sameer Singh",
"Niranjan Balasubramanian"
] | We introduce entity post-modifier generation as an instance of a collaborative writing task. Given a sentence about a target entity, the task is to automatically generate a post-modifier phrase that provides contextually relevant information about the entity. For example, for the sentence, “Barack Obama, _______, suppo... | N19-1089 | 10.18653/v1/N19-1089 | null | 1904.03111 | title_snapshot |
N19-1090 | Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting | https://aclanthology.org/N19-1090/ | [
"J. Edward Hu",
"Huda Khayrallah",
"Ryan Culkin",
"Patrick Xia",
"Tongfei Chen",
"Matt Post",
"Benjamin Van Durme"
] | Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in generation tasks such as machine translation or monolingual text rewriting. We describe vectorized dynamic beam allocation, which extends work in lexically-constrained decod... | N19-1090 | 10.18653/v1/N19-1090 | null | null | null |
N19-1091 | Courteously Yours: Inducing courteous behavior in Customer Care responses using Reinforced Pointer Generator Network | https://aclanthology.org/N19-1091/ | [
"Hitesh Golchha",
"Mauajama Firdaus",
"Asif Ekbal",
"Pushpak Bhattacharyya"
] | In this paper, we propose an effective deep learning framework for inducing courteous behavior in customer care responses. The interaction between a customer and the customer care representative contributes substantially to the overall customer experience. Thus it is imperative for customer care agents and chatbots eng... | N19-1091 | 10.18653/v1/N19-1091 | null | null | null |
N19-1092 | How to Avoid Sentences Spelling Boring? Towards a Neural Approach to Unsupervised Metaphor Generation | https://aclanthology.org/N19-1092/ | [
"Zhiwei Yu",
"Xiaojun Wan"
] | Metaphor generation attempts to replicate human creativity with language, which is an attractive but challengeable text generation task. Previous efforts mainly focus on template-based or rule-based methods and result in a lack of linguistic subtlety. In order to create novel metaphors, we propose a neural approach to ... | N19-1092 | 10.18653/v1/N19-1092 | null | null | null |
N19-1093 | Incorporating Context and External Knowledge for Pronoun Coreference Resolution | https://aclanthology.org/N19-1093/ | [
"Hongming Zhang",
"Yan Song",
"Yangqiu Song"
] | Linking pronominal expressions to the correct references requires, in many cases, better analysis of the contextual information and external knowledge. In this paper, we propose a two-layer model for pronoun coreference resolution that leverages both context and external knowledge, where a knowledge attention mechanism... | N19-1093 | 10.18653/v1/N19-1093 | null | 1905.10238 | title_snapshot |
N19-1094 | Unsupervised Deep Structured Semantic Models for Commonsense Reasoning | https://aclanthology.org/N19-1094/ | [
"Shuohang Wang",
"Sheng Zhang",
"Yelong Shen",
"Xiaodong Liu",
"Jingjing Liu",
"Jianfeng Gao",
"Jing Jiang"
] | Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning commonsense knowledge from a large amount of raw text via unsupervised learning. We propose two neural network models based on the Deep Struct... | N19-1094 | 10.18653/v1/N19-1094 | null | 1904.01938 | title_snapshot |
N19-1095 | Recovering dropped pronouns in Chinese conversations via modeling their referents | https://aclanthology.org/N19-1095/ | [
"Jingxuan Yang",
"Jianzhuo Tong",
"Si Li",
"Sheng Gao",
"Jun Guo",
"Nianwen Xue"
] | Pronouns are often dropped in Chinese sentences, and this happens more frequently in conversational genres as their referents can be easily understood from context. Recovering dropped pronouns is essential to applications such as Information Extraction where the referents of these dropped pronouns need to be resolved, ... | N19-1095 | 10.18653/v1/N19-1095 | null | 1906.02128 | title_snapshot |
N19-1096 | The problem with probabilistic DAG automata for semantic graphs | https://aclanthology.org/N19-1096/ | [
"Ieva Vasiljeva",
"Sorcha Gilroy",
"Adam Lopez"
] | Semantic representations in the form of directed acyclic graphs (DAGs) have been introduced in recent years, and to model them, we need probabilistic models of DAGs. One model that has attracted some attention is the DAG automaton, but it has not been studied as a probabilistic model. We show that some DAG automata can... | N19-1096 | 10.18653/v1/N19-1096 | null | 1810.12266 | title_snapshot |
N19-1097 | A Systematic Study of Leveraging Subword Information for Learning Word Representations | https://aclanthology.org/N19-1097/ | [
"Yi Zhu",
"Ivan Vulić",
"Anna Korhonen"
] | The use of subword-level information (e.g., characters, character n-grams, morphemes) has become ubiquitous in modern word representation learning. Its importance is attested especially for morphologically rich languages which generate a large number of rare words. Despite a steadily increasing interest in such subword... | N19-1097 | 10.18653/v1/N19-1097 | null | 1904.07994 | title_snapshot |
N19-1098 | Better Word Embeddings by Disentangling Contextual n-Gram Information | https://aclanthology.org/N19-1098/ | [
"Prakhar Gupta",
"Matteo Pagliardini",
"Martin Jaggi"
] | Pre-trained word vectors are ubiquitous in Natural Language Processing applications. In this paper, we show how training word embeddings jointly with bigram and even trigram embeddings, results in improved unigram embeddings. We claim that training word embeddings along with higher n-gram embeddings helps in the remova... | N19-1098 | 10.18653/v1/N19-1098 | null | 1904.05033 | title_snapshot |
N19-1099 | Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical Dirichlet Process | https://aclanthology.org/N19-1099/ | [
"Dingcheng Li",
"Siamak Zamani",
"Jingyuan Zhang",
"Ping Li"
] | Leveraging domain knowledge is an effective strategy for enhancing the quality of inferred low-dimensional representations of documents by topic models. In this paper, we develop topic modeling with knowledge graph embedding (TMKGE), a Bayesian nonparametric model to employ knowledge graph (KG) embedding in the context... | N19-1099 | 10.18653/v1/N19-1099 | null | null | null |
N19-1100 | Correlation Coefficients and Semantic Textual Similarity | https://aclanthology.org/N19-1100/ | [
"Vitalii Zhelezniak",
"Aleksandar Savkov",
"April Shen",
"Nils Hammerla"
] | A large body of research into semantic textual similarity has focused on constructing state-of-the-art embeddings using sophisticated modelling, careful choice of learning signals and many clever tricks. By contrast, little attention has been devoted to similarity measures between these embeddings, with cosine similari... | N19-1100 | 10.18653/v1/N19-1100 | null | 1905.07790 | title_snapshot |
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