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 21 132 | paper_url stringlengths 34 34 | authors listlengths 1 9 | abstract large_stringlengths 303 1.49k | anthology_id stringlengths 8 8 | doi stringlengths 20 20 | award stringclasses 0
values | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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P17-1001 | Adversarial Multi-task Learning for Text Classification | https://aclanthology.org/P17-1001/ | [
"Pengfei Liu",
"Xipeng Qiu",
"Xuanjing Huang"
] | Neural network models have shown their promising opportunities for multi-task learning, which focus on learning the shared layers to extract the common and task-invariant features. However, in most existing approaches, the extracted shared features are prone to be contaminated by task-specific features or the noise bro... | P17-1001 | 10.18653/v1/P17-1001 | null | 1704.05742 | title_snapshot |
P17-1002 | Neural End-to-End Learning for Computational Argumentation Mining | https://aclanthology.org/P17-1002/ | [
"Steffen Eger",
"Johannes Daxenberger",
"Iryna Gurevych"
] | We investigate neural techniques for end-to-end computational argumentation mining (AM). We frame AM both as a token-based dependency parsing and as a token-based sequence tagging problem, including a multi-task learning setup. Contrary to models that operate on the argument component level, we find that framing AM as ... | P17-1002 | 10.18653/v1/P17-1002 | null | 1704.06104 | title_snapshot |
P17-1003 | Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision | https://aclanthology.org/P17-1003/ | [
"Chen Liang",
"Jonathan Berant",
"Quoc Le",
"Kenneth D. Forbus",
"Ni Lao"
] | Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this work, we introduce a Neural Symbolic Machine, which contains (a) a neural “programmer”, i.e., a sequ... | P17-1003 | 10.18653/v1/P17-1003 | null | 1611.00020 | title_snapshot |
P17-1004 | Neural Relation Extraction with Multi-lingual Attention | https://aclanthology.org/P17-1004/ | [
"Yankai Lin",
"Zhiyuan Liu",
"Maosong Sun"
] | Relation extraction has been widely used for finding unknown relational facts from plain text. Most existing methods focus on exploiting mono-lingual data for relation extraction, ignoring massive information from the texts in various languages. To address this issue, we introduce a multi-lingual neural relation extrac... | P17-1004 | 10.18653/v1/P17-1004 | null | null | null |
P17-1005 | Learning Structured Natural Language Representations for Semantic Parsing | https://aclanthology.org/P17-1005/ | [
"Jianpeng Cheng",
"Siva Reddy",
"Vijay Saraswat",
"Mirella Lapata"
] | We introduce a neural semantic parser which is interpretable and scalable. Our model converts natural language utterances to intermediate, domain-general natural language representations in the form of predicate-argument structures, which are induced with a transition system and subsequently mapped to target domains. T... | P17-1005 | 10.18653/v1/P17-1005 | null | 1704.08387 | title_snapshot |
P17-1006 | Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules | https://aclanthology.org/P17-1006/ | [
"Ivan Vulić",
"Nikola Mrkšić",
"Roi Reichart",
"Diarmuid Ó Séaghdha",
"Steve Young",
"Anna Korhonen"
] | Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms; and 2) insensitivity to distinct lexical relations that have similar distributional signatures. These effects are detrimental for language ... | P17-1006 | 10.18653/v1/P17-1006 | null | 1706.00377 | title_snapshot |
P17-1007 | Skip-Gram − Zipf + Uniform = Vector Additivity | https://aclanthology.org/P17-1007/ | [
"Alex Gittens",
"Dimitris Achlioptas",
"Michael W. Mahoney"
] | In recent years word-embedding models have gained great popularity due to their remarkable performance on several tasks, including word analogy questions and caption generation. An unexpected “side-effect” of such models is that their vectors often exhibit compositionality, i.e., adding two word-vectors results in a ve... | P17-1007 | 10.18653/v1/P17-1007 | null | null | null |
P17-1008 | The State of the Art in Semantic Representation | https://aclanthology.org/P17-1008/ | [
"Omri Abend",
"Ari Rappoport"
] | Semantic representation is receiving growing attention in NLP in the past few years, and many proposals for semantic schemes (e.g., AMR, UCCA, GMB, UDS) have been put forth. Yet, little has been done to assess the achievements and the shortcomings of these new contenders, compare them with syntactic schemes, and clarif... | P17-1008 | 10.18653/v1/P17-1008 | null | null | null |
P17-1009 | Joint Learning for Event Coreference Resolution | https://aclanthology.org/P17-1009/ | [
"Jing Lu",
"Vincent Ng"
] | While joint models have been developed for many NLP tasks, the vast majority of event coreference resolvers, including the top-performing resolvers competing in the recent TAC KBP 2016 Event Nugget Detection and Coreference task, are pipeline-based, where the propagation of errors from the trigger detection component t... | P17-1009 | 10.18653/v1/P17-1009 | null | null | null |
P17-1010 | Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun Resolution | https://aclanthology.org/P17-1010/ | [
"Ting Liu",
"Yiming Cui",
"Qingyu Yin",
"Wei-Nan Zhang",
"Shijin Wang",
"Guoping Hu"
] | Most existing approaches for zero pronoun resolution are heavily relying on annotated data, which is often released by shared task organizers. Therefore, the lack of annotated data becomes a major obstacle in the progress of zero pronoun resolution task. Also, it is expensive to spend manpower on labeling the data for ... | P17-1010 | 10.18653/v1/P17-1010 | null | 1606.01603 | title_snapshot |
P17-1011 | Discourse Mode Identification in Essays | https://aclanthology.org/P17-1011/ | [
"Wei Song",
"Dong Wang",
"Ruiji Fu",
"Lizhen Liu",
"Ting Liu",
"Guoping Hu"
] | Discourse modes play an important role in writing composition and evaluation. This paper presents a study on the manual and automatic identification of narration,exposition, description, argument and emotion expressing sentences in narrative essays. We annotate a corpus to study the characteristics of discourse modes a... | P17-1011 | 10.18653/v1/P17-1011 | null | null | null |
P17-1012 | A Convolutional Encoder Model for Neural Machine Translation | https://aclanthology.org/P17-1012/ | [
"Jonas Gehring",
"Michael Auli",
"David Grangier",
"Yann Dauphin"
] | The prevalent approach to neural machine translation relies on bi-directional LSTMs to encode the source sentence. We present a faster and simpler architecture based on a succession of convolutional layers. This allows to encode the source sentence simultaneously compared to recurrent networks for which computation is ... | P17-1012 | 10.18653/v1/P17-1012 | null | 1611.02344 | title_snapshot |
P17-1013 | Deep Neural Machine Translation with Linear Associative Unit | https://aclanthology.org/P17-1013/ | [
"Mingxuan Wang",
"Zhengdong Lu",
"Jie Zhou",
"Qun Liu"
] | Deep Neural Networks (DNNs) have provably enhanced the state-of-the-art Neural Machine Translation (NMT) with its capability in modeling complex functions and capturing complex linguistic structures. However NMT with deep architecture in its encoder or decoder RNNs often suffer from severe gradient diffusion due to the... | P17-1013 | 10.18653/v1/P17-1013 | null | 1705.00861 | title_snapshot |
P17-1014 | Neural AMR: Sequence-to-Sequence Models for Parsing and Generation | https://aclanthology.org/P17-1014/ | [
"Ioannis Konstas",
"Srinivasan Iyer",
"Mark Yatskar",
"Yejin Choi",
"Luke Zettlemoyer"
] | Sequence-to-sequence models have shown strong performance across a broad range of applications. However, their application to parsing and generating text using Abstract Meaning Representation (AMR) has been limited, due to the relatively limited amount of labeled data and the non-sequential nature of the AMR graphs. We... | P17-1014 | 10.18653/v1/P17-1014 | null | 1704.08381 | title_snapshot |
P17-1015 | Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems | https://aclanthology.org/P17-1015/ | [
"Wang Ling",
"Dani Yogatama",
"Chris Dyer",
"Phil Blunsom"
] | Solving algebraic word problems requires executing a series of arithmetic operations—a program—to obtain a final answer. However, since programs can be arbitrarily complicated, inducing them directly from question-answer pairs is a formidable challenge. To make this task more feasible, we solve these problems by genera... | P17-1015 | 10.18653/v1/P17-1015 | null | 1705.04146 | title_snapshot |
P17-1016 | Automatically Generating Rhythmic Verse with Neural Networks | https://aclanthology.org/P17-1016/ | [
"Jack Hopkins",
"Douwe Kiela"
] | We propose two novel methodologies for the automatic generation of rhythmic poetry in a variety of forms. The first approach uses a neural language model trained on a phonetic encoding to learn an implicit representation of both the form and content of English poetry. This model can effectively learn common poetic devi... | P17-1016 | 10.18653/v1/P17-1016 | null | null | null |
P17-1017 | Creating Training Corpora for NLG Micro-Planners | https://aclanthology.org/P17-1017/ | [
"Claire Gardent",
"Anastasia Shimorina",
"Shashi Narayan",
"Laura Perez-Beltrachini"
] | In this paper, we present a novel framework for semi-automatically creating linguistically challenging micro-planning data-to-text corpora from existing Knowledge Bases. Because our method pairs data of varying size and shape with texts ranging from simple clauses to short texts, a dataset created using this framework ... | P17-1017 | 10.18653/v1/P17-1017 | null | null | null |
P17-1018 | Gated Self-Matching Networks for Reading Comprehension and Question Answering | https://aclanthology.org/P17-1018/ | [
"Wenhui Wang",
"Nan Yang",
"Furu Wei",
"Baobao Chang",
"Ming Zhou"
] | In this paper, we present the gated self-matching networks for reading comprehension style question answering, which aims to answer questions from a given passage. We first match the question and passage with gated attention-based recurrent networks to obtain the question-aware passage representation. Then we propose a... | P17-1018 | 10.18653/v1/P17-1018 | null | null | null |
P17-1019 | Generating Natural Answers by Incorporating Copying and Retrieving Mechanisms in Sequence-to-Sequence Learning | https://aclanthology.org/P17-1019/ | [
"Shizhu He",
"Cao Liu",
"Kang Liu",
"Jun Zhao"
] | Generating answer with natural language sentence is very important in real-world question answering systems, which needs to obtain a right answer as well as a coherent natural response. In this paper, we propose an end-to-end question answering system called COREQA in sequence-to-sequence learning, which incorporates c... | P17-1019 | 10.18653/v1/P17-1019 | null | null | null |
P17-1020 | Coarse-to-Fine Question Answering for Long Documents | https://aclanthology.org/P17-1020/ | [
"Eunsol Choi",
"Daniel Hewlett",
"Jakob Uszkoreit",
"Illia Polosukhin",
"Alexandre Lacoste",
"Jonathan Berant"
] | We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models. While most successful approaches for reading comprehension rely on recurrent neural networks (RNNs), running them over long documents is prohibitively ... | P17-1020 | 10.18653/v1/P17-1020 | null | null | null |
P17-1021 | An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge | https://aclanthology.org/P17-1021/ | [
"Yanchao Hao",
"Yuanzhe Zhang",
"Kang Liu",
"Shizhu He",
"Zhanyi Liu",
"Hua Wu",
"Jun Zhao"
] | With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important. Question answering over knowledge base (KB-QA) is one of the promising approaches to access the substantial knowledge. Meanwhile, as the neural network-based (NN-based) methods develop, NN-based... | P17-1021 | 10.18653/v1/P17-1021 | null | 1606.00979 | title_judge |
P17-1022 | Translating Neuralese | https://aclanthology.org/P17-1022/ | [
"Jacob Andreas",
"Anca Dragan",
"Dan Klein"
] | Several approaches have recently been proposed for learning decentralized deep multiagent policies that coordinate via a differentiable communication channel. While these policies are effective for many tasks, interpretation of their induced communication strategies has remained a challenge. Here we propose to interpre... | P17-1022 | 10.18653/v1/P17-1022 | null | 1704.06960 | title_snapshot |
P17-1023 | Obtaining referential word meanings from visual and distributional information: Experiments on object naming | https://aclanthology.org/P17-1023/ | [
"Sina Zarrieß",
"David Schlangen"
] | We investigate object naming, which is an important sub-task of referring expression generation on real-world images. As opposed to mutually exclusive labels used in object recognition, object names are more flexible, subject to communicative preferences and semantically related to each other. Therefore, we investigate... | P17-1023 | 10.18653/v1/P17-1023 | null | null | null |
P17-1024 | FOIL it! Find One mismatch between Image and Language caption | https://aclanthology.org/P17-1024/ | [
"Ravi Shekhar",
"Sandro Pezzelle",
"Yauhen Klimovich",
"Aurélie Herbelot",
"Moin Nabi",
"Enver Sangineto",
"Raffaella Bernardi"
] | In this paper, we aim to understand whether current language and vision (LaVi) models truly grasp the interaction between the two modalities. To this end, we propose an extension of the MS-COCO dataset, FOIL-COCO, which associates images with both correct and ‘foil’ captions, that is, descriptions of the image that are... | P17-1024 | 10.18653/v1/P17-1024 | null | 1705.01359 | title_snapshot |
P17-1025 | Verb Physics: Relative Physical Knowledge of Actions and Objects | https://aclanthology.org/P17-1025/ | [
"Maxwell Forbes",
"Yejin Choi"
] | Learning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, e.g., “My house is bigger than me.” However, while rarely stated explicitly, this trivial everyday knowledge does influence the way people talk about the world, which provides indirect clues t... | P17-1025 | 10.18653/v1/P17-1025 | null | 1706.03799 | title_snapshot |
P17-1026 | A* CCG Parsing with a Supertag and Dependency Factored Model | https://aclanthology.org/P17-1026/ | [
"Masashi Yoshikawa",
"Hiroshi Noji",
"Yuji Matsumoto"
] | We propose a new A* CCG parsing model in which the probability of a tree is decomposed into factors of CCG categories and its syntactic dependencies both defined on bi-directional LSTMs. Our factored model allows the precomputation of all probabilities and runs very efficiently, while modeling sentence structures expli... | P17-1026 | 10.18653/v1/P17-1026 | null | 1704.06936 | title_snapshot |
P17-1027 | A Full Non-Monotonic Transition System for Unrestricted Non-Projective Parsing | https://aclanthology.org/P17-1027/ | [
"Daniel Fernández-González",
"Carlos Gómez-Rodríguez"
] | Restricted non-monotonicity has been shown beneficial for the projective arc-eager dependency parser in previous research, as posterior decisions can repair mistakes made in previous states due to the lack of information. In this paper, we propose a novel, fully non-monotonic transition system based on the non-projecti... | P17-1027 | 10.18653/v1/P17-1027 | null | 1706.03367 | title_snapshot |
P17-1028 | Aggregating and Predicting Sequence Labels from Crowd Annotations | https://aclanthology.org/P17-1028/ | [
"An Thanh Nguyen",
"Byron Wallace",
"Junyi Jessy Li",
"Ani Nenkova",
"Matthew Lease"
] | Despite sequences being core to NLP, scant work has considered how to handle noisy sequence labels from multiple annotators for the same text. Given such annotations, we consider two complementary tasks: (1) aggregating sequential crowd labels to infer a best single set of consensus annotations; and (2) using crowd ann... | P17-1028 | 10.18653/v1/P17-1028 | null | null | null |
P17-1029 | Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction | https://aclanthology.org/P17-1029/ | [
"Chunting Zhou",
"Graham Neubig"
] | Labeled sequence transduction is a task of transforming one sequence into another sequence that satisfies desiderata specified by a set of labels. In this paper we propose multi-space variational encoder-decoders, a new model for labeled sequence transduction with semi-supervised learning. The generative model can use ... | P17-1029 | 10.18653/v1/P17-1029 | null | 1704.01691 | title_snapshot |
P17-1030 | Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling | https://aclanthology.org/P17-1030/ | [
"Zhe Gan",
"Chunyuan Li",
"Changyou Chen",
"Yunchen Pu",
"Qinliang Su",
"Lawrence Carin"
] | Recurrent neural networks (RNNs) have shown promising performance for language modeling. However, traditional training of RNNs using back-propagation through time often suffers from overfitting. One reason for this is that stochastic optimization (used for large training sets) does not provide good estimates of model u... | P17-1030 | 10.18653/v1/P17-1030 | null | 1611.08034 | title_snapshot |
P17-1031 | Learning attention for historical text normalization by learning to pronounce | https://aclanthology.org/P17-1031/ | [
"Marcel Bollmann",
"Joachim Bingel",
"Anders Søgaard"
] | Automated processing of historical texts often relies on pre-normalization to modern word forms. Training encoder-decoder architectures to solve such problems typically requires a lot of training data, which is not available for the named task. We address this problem by using several novel encoder-decoder architecture... | P17-1031 | 10.18653/v1/P17-1031 | null | null | null |
P17-1032 | Deep Learning in Semantic Kernel Spaces | https://aclanthology.org/P17-1032/ | [
"Danilo Croce",
"Simone Filice",
"Giuseppe Castellucci",
"Roberto Basili"
] | Kernel methods enable the direct usage of structured representations of textual data during language learning and inference tasks. Expressive kernels, such as Tree Kernels, achieve excellent performance in NLP. On the other side, deep neural networks have been demonstrated effective in automatically learning feature re... | P17-1032 | 10.18653/v1/P17-1032 | null | null | null |
P17-1033 | Topically Driven Neural Language Model | https://aclanthology.org/P17-1033/ | [
"Jey Han Lau",
"Timothy Baldwin",
"Trevor Cohn"
] | Language models are typically applied at the sentence level, without access to the broader document context. We present a neural language model that incorporates document context in the form of a topic model-like architecture, thus providing a succinct representation of the broader document context outside of the curre... | P17-1033 | 10.18653/v1/P17-1033 | null | 1704.08012 | title_snapshot |
P17-1034 | Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors | https://aclanthology.org/P17-1034/ | [
"Xuepeng Wang",
"Kang Liu",
"Jun Zhao"
] | Solving cold-start problem in review spam detection is an urgent and significant task. It can help the on-line review websites to relieve the damage of spammers in time, but has never been investigated by previous work. This paper proposes a novel neural network model to detect review spam for cold-start problem, by le... | P17-1034 | 10.18653/v1/P17-1034 | null | null | null |
P17-1035 | Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network | https://aclanthology.org/P17-1035/ | [
"Abhijit Mishra",
"Kuntal Dey",
"Pushpak Bhattacharyya"
] | Cognitive NLP systems- i.e., NLP systems that make use of behavioral data - augment traditional text-based features with cognitive features extracted from eye-movement patterns, EEG signals, brain-imaging etc. Such extraction of features is typically manual. We contend that manual extraction of features may not be the ... | P17-1035 | 10.18653/v1/P17-1035 | null | null | null |
P17-1036 | An Unsupervised Neural Attention Model for Aspect Extraction | https://aclanthology.org/P17-1036/ | [
"Ruidan He",
"Wee Sun Lee",
"Hwee Tou Ng",
"Daniel Dahlmeier"
] | Aspect extraction is an important and challenging task in aspect-based sentiment analysis. Existing works tend to apply variants of topic models on this task. While fairly successful, these methods usually do not produce highly coherent aspects. In this paper, we present a novel neural approach with the aim of discover... | P17-1036 | 10.18653/v1/P17-1036 | null | null | null |
P17-1037 | Other Topics You May Also Agree or Disagree: Modeling Inter-Topic Preferences using Tweets and Matrix Factorization | https://aclanthology.org/P17-1037/ | [
"Akira Sasaki",
"Kazuaki Hanawa",
"Naoaki Okazaki",
"Kentaro Inui"
] | We presents in this paper our approach for modeling inter-topic preferences of Twitter users: for example, “those who agree with the Trans-Pacific Partnership (TPP) also agree with free trade”. This kind of knowledge is useful not only for stance detection across multiple topics but also for various real-world applicat... | P17-1037 | 10.18653/v1/P17-1037 | null | 1704.07986 | title_snapshot |
P17-1038 | Automatically Labeled Data Generation for Large Scale Event Extraction | https://aclanthology.org/P17-1038/ | [
"Yubo Chen",
"Shulin Liu",
"Xiang Zhang",
"Kang Liu",
"Jun Zhao"
] | Modern models of event extraction for tasks like ACE are based on supervised learning of events from small hand-labeled data. However, hand-labeled training data is expensive to produce, in low coverage of event types, and limited in size, which makes supervised methods hard to extract large scale of events for knowled... | P17-1038 | 10.18653/v1/P17-1038 | null | null | null |
P17-1039 | Time Expression Analysis and Recognition Using Syntactic Token Types and General Heuristic Rules | https://aclanthology.org/P17-1039/ | [
"Xiaoshi Zhong",
"Aixin Sun",
"Erik Cambria"
] | Extracting time expressions from free text is a fundamental task for many applications. We analyze the time expressions from four datasets and find that only a small group of words are used to express time information, and the words in time expressions demonstrate similar syntactic behaviour. Based on the findings, we ... | P17-1039 | 10.18653/v1/P17-1039 | null | null | null |
P17-1040 | Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix | https://aclanthology.org/P17-1040/ | [
"Bingfeng Luo",
"Yansong Feng",
"Zheng Wang",
"Zhanxing Zhu",
"Songfang Huang",
"Rui Yan",
"Dongyan Zhao"
] | Distant supervision significantly reduces human efforts in building training data for many classification tasks. While promising, this technique often introduces noise to the generated training data, which can severely affect the model performance. In this paper, we take a deep look at the application of distant superv... | P17-1040 | 10.18653/v1/P17-1040 | null | 1705.03995 | title_snapshot |
P17-1041 | A Syntactic Neural Model for General-Purpose Code Generation | https://aclanthology.org/P17-1041/ | [
"Pengcheng Yin",
"Graham Neubig"
] | We consider the problem of parsing natural language descriptions into source code written in a general-purpose programming language like Python. Existing data-driven methods treat this problem as a language generation task without considering the underlying syntax of the target programming language. Informed by previou... | P17-1041 | 10.18653/v1/P17-1041 | null | 1704.01696 | title_snapshot |
P17-1042 | Learning bilingual word embeddings with (almost) no bilingual data | https://aclanthology.org/P17-1042/ | [
"Mikel Artetxe",
"Gorka Labaka",
"Eneko Agirre"
] | Most methods to learn bilingual word embeddings rely on large parallel corpora, which is difficult to obtain for most language pairs. This has motivated an active research line to relax this requirement, with methods that use document-aligned corpora or bilingual dictionaries of a few thousand words instead. In this wo... | P17-1042 | 10.18653/v1/P17-1042 | null | null | null |
P17-1043 | Abstract Meaning Representation Parsing using LSTM Recurrent Neural Networks | https://aclanthology.org/P17-1043/ | [
"William Foland",
"James H. Martin"
] | We present a system which parses sentences into Abstract Meaning Representations, improving state-of-the-art results for this task by more than 5%. AMR graphs represent semantic content using linguistic properties such as semantic roles, coreference, negation, and more. The AMR parser does not rely on a syntactic pre-p... | P17-1043 | 10.18653/v1/P17-1043 | null | null | null |
P17-1044 | Deep Semantic Role Labeling: What Works and What’s Next | https://aclanthology.org/P17-1044/ | [
"Luheng He",
"Kenton Lee",
"Mike Lewis",
"Luke Zettlemoyer"
] | We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations. We use a deep highway BiLSTM architecture with constrained decoding, while observing a number of recent best practices for initi... | P17-1044 | 10.18653/v1/P17-1044 | null | null | null |
P17-1045 | Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access | https://aclanthology.org/P17-1045/ | [
"Bhuwan Dhingra",
"Lihong Li",
"Xiujun Li",
"Jianfeng Gao",
"Yun-Nung Chen",
"Faisal Ahmed",
"Li Deng"
] | This paper proposes KB-InfoBot - a multi-turn dialogue agent which helps users search Knowledge Bases (KBs) without composing complicated queries. Such goal-oriented dialogue agents typically need to interact with an external database to access real-world knowledge. Previous systems achieved this by issuing a symbolic ... | P17-1045 | 10.18653/v1/P17-1045 | null | 1609.00777 | title_snapshot |
P17-1046 | Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots | https://aclanthology.org/P17-1046/ | [
"Yu Wu",
"Wei Wu",
"Chen Xing",
"Ming Zhou",
"Zhoujun Li"
] | We study response selection for multi-turn conversation in retrieval based chatbots. Existing work either concatenates utterances in context or matches a response with a highly abstract context vector finally, which may lose relationships among the utterances or important information in the context. We propose a sequen... | P17-1046 | 10.18653/v1/P17-1046 | null | 1612.01627 | title_snapshot |
P17-1047 | Learning Word-Like Units from Joint Audio-Visual Analysis | https://aclanthology.org/P17-1047/ | [
"David Harwath",
"James Glass"
] | Given a collection of images and spoken audio captions, we present a method for discovering word-like acoustic units in the continuous speech signal and grounding them to semantically relevant image regions. For example, our model is able to detect spoken instances of the word ‘lighthouse’ within an utterance and assoc... | P17-1047 | 10.18653/v1/P17-1047 | null | 1701.07481 | title_snapshot |
P17-1048 | Joint CTC/attention decoding for end-to-end speech recognition | https://aclanthology.org/P17-1048/ | [
"Takaaki Hori",
"Shinji Watanabe",
"John Hershey"
] | End-to-end automatic speech recognition (ASR) has become a popular alternative to conventional DNN/HMM systems because it avoids the need for linguistic resources such as pronunciation dictionary, tokenization, and context-dependency trees, leading to a greatly simplified model-building process. There are two major typ... | P17-1048 | 10.18653/v1/P17-1048 | null | null | null |
P17-1049 | Found in Translation: Reconstructing Phylogenetic Language Trees from Translations | https://aclanthology.org/P17-1049/ | [
"Ella Rabinovich",
"Noam Ordan",
"Shuly Wintner"
] | Translation has played an important role in trade, law, commerce, politics, and literature for thousands of years. Translators have always tried to be invisible; ideal translations should look as if they were written originally in the target language. We show that traces of the source language remain in the translation... | P17-1049 | 10.18653/v1/P17-1049 | null | 1704.07146 | title_snapshot |
P17-1050 | Predicting Native Language from Gaze | https://aclanthology.org/P17-1050/ | [
"Yevgeni Berzak",
"Chie Nakamura",
"Suzanne Flynn",
"Boris Katz"
] | A fundamental question in language learning concerns the role of a speaker’s first language in second language acquisition. We present a novel methodology for studying this question: analysis of eye-movement patterns in second language reading of free-form text. Using this methodology, we demonstrate for the first time... | P17-1050 | 10.18653/v1/P17-1050 | null | 1704.07398 | title_snapshot |
P17-1051 | MORSE: Semantic-ally Drive-n MORpheme SEgment-er | https://aclanthology.org/P17-1051/ | [
"Tarek Sakakini",
"Suma Bhat",
"Pramod Viswanath"
] | We present in this paper a novel framework for morpheme segmentation which uses the morpho-syntactic regularities preserved by word representations, in addition to orthographic features, to segment words into morphemes. This framework is the first to consider vocabulary-wide syntactico-semantic information for this tas... | P17-1051 | 10.18653/v1/P17-1051 | null | 1702.02212 | title_snapshot |
P17-1052 | Deep Pyramid Convolutional Neural Networks for Text Categorization | https://aclanthology.org/P17-1052/ | [
"Rie Johnson",
"Tong Zhang"
] | This paper proposes a low-complexity word-level deep convolutional neural network (CNN) architecture for text categorization that can efficiently represent long-range associations in text. In the literature, several deep and complex neural networks have been proposed for this task, assuming availability of relatively l... | P17-1052 | 10.18653/v1/P17-1052 | null | null | null |
P17-1053 | Improved Neural Relation Detection for Knowledge Base Question Answering | https://aclanthology.org/P17-1053/ | [
"Mo Yu",
"Wenpeng Yin",
"Kazi Saidul Hasan",
"Cicero dos Santos",
"Bing Xiang",
"Bowen Zhou"
] | Relation detection is a core component of many NLP applications including Knowledge Base Question Answering (KBQA). In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning which detects KB relations given an input question. Our method uses deep residual bidirectional LSTMs to com... | P17-1053 | 10.18653/v1/P17-1053 | null | 1704.06194 | title_snapshot |
P17-1054 | Deep Keyphrase Generation | https://aclanthology.org/P17-1054/ | [
"Rui Meng",
"Sanqiang Zhao",
"Shuguang Han",
"Daqing He",
"Peter Brusilovsky",
"Yu Chi"
] | Keyphrase provides highly-summative information that can be effectively used for understanding, organizing and retrieving text content. Though previous studies have provided many workable solutions for automated keyphrase extraction, they commonly divided the to-be-summarized content into multiple text chunks, then ran... | P17-1054 | 10.18653/v1/P17-1054 | null | 1704.06879 | title_snapshot |
P17-1055 | Attention-over-Attention Neural Networks for Reading Comprehension | https://aclanthology.org/P17-1055/ | [
"Yiming Cui",
"Zhipeng Chen",
"Si Wei",
"Shijin Wang",
"Ting Liu",
"Guoping Hu"
] | Cloze-style reading comprehension is a representative problem in mining relationship between document and query. In this paper, we present a simple but novel model called attention-over-attention reader for better solving cloze-style reading comprehension task. The proposed model aims to place another attention mechani... | P17-1055 | 10.18653/v1/P17-1055 | null | 1607.04423 | title_snapshot |
P17-1056 | Alignment at Work: Using Language to Distinguish the Internalization and Self-Regulation Components of Cultural Fit in Organizations | https://aclanthology.org/P17-1056/ | [
"Gabriel Doyle",
"Amir Goldberg",
"Sameer Srivastava",
"Michael Frank"
] | Cultural fit is widely believed to affect the success of individuals and the groups to which they belong. Yet it remains an elusive, poorly measured construct. Recent research draws on computational linguistics to measure cultural fit but overlooks asymmetries in cultural adaptation. By contrast, we develop a directed,... | P17-1056 | 10.18653/v1/P17-1056 | null | null | null |
P17-1057 | Representations of language in a model of visually grounded speech signal | https://aclanthology.org/P17-1057/ | [
"Grzegorz Chrupała",
"Lieke Gelderloos",
"Afra Alishahi"
] | We present a visually grounded model of speech perception which projects spoken utterances and images to a joint semantic space. We use a multi-layer recurrent highway network to model the temporal nature of spoken speech, and show that it learns to extract both form and meaning-based linguistic knowledge from the inpu... | P17-1057 | 10.18653/v1/P17-1057 | null | 1702.01991 | title_snapshot |
P17-1058 | Spectral Analysis of Information Density in Dialogue Predicts Collaborative Task Performance | https://aclanthology.org/P17-1058/ | [
"Yang Xu",
"David Reitter"
] | We propose a perspective on dialogue that focuses on relative information contributions of conversation partners as a key to successful communication. We predict the success of collaborative task in English and Danish corpora of task-oriented dialogue. Two features are extracted from the frequency domain representation... | P17-1058 | 10.18653/v1/P17-1058 | null | null | null |
P17-1059 | Affect-LM: A Neural Language Model for Customizable Affective Text Generation | https://aclanthology.org/P17-1059/ | [
"Sayan Ghosh",
"Mathieu Chollet",
"Eugene Laksana",
"Louis-Philippe Morency",
"Stefan Scherer"
] | Human verbal communication includes affective messages which are conveyed through use of emotionally colored words. There has been a lot of research effort in this direction but the problem of integrating state-of-the-art neural language models with affective information remains an area ripe for exploration. In this pa... | P17-1059 | 10.18653/v1/P17-1059 | null | 1704.06851 | title_snapshot |
P17-1060 | Domain Attention with an Ensemble of Experts | https://aclanthology.org/P17-1060/ | [
"Young-Bum Kim",
"Karl Stratos",
"Dongchan Kim"
] | An important problem in domain adaptation is to quickly generalize to a new domain with limited supervision given K existing domains. One approach is to retrain a global model across all K + 1 domains using standard techniques, for instance Daumé III (2009). However, it is desirable to adapt without having to re-estima... | P17-1060 | 10.18653/v1/P17-1060 | null | null | null |
P17-1061 | Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders | https://aclanthology.org/P17-1061/ | [
"Tiancheng Zhao",
"Ran Zhao",
"Maxine Eskenazi"
] | While recent neural encoder-decoder models have shown great promise in modeling open-domain conversations, they often generate dull and generic responses. Unlike past work that has focused on diversifying the output of the decoder from word-level to alleviate this problem, we present a novel framework based on conditio... | P17-1061 | 10.18653/v1/P17-1061 | null | 1703.10960 | title_snapshot |
P17-1062 | Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning | https://aclanthology.org/P17-1062/ | [
"Jason D. Williams",
"Kavosh Asadi",
"Geoffrey Zweig"
] | End-to-end learning of recurrent neural networks (RNNs) is an attractive solution for dialog systems; however, current techniques are data-intensive and require thousands of dialogs to learn simple behaviors. We introduce Hybrid Code Networks (HCNs), which combine an RNN with domain-specific knowledge encoded as softwa... | P17-1062 | 10.18653/v1/P17-1062 | null | 1702.03274 | title_snapshot |
P17-1063 | Generating Contrastive Referring Expressions | https://aclanthology.org/P17-1063/ | [
"Martín Villalba",
"Christoph Teichmann",
"Alexander Koller"
] | The referring expressions (REs) produced by a natural language generation (NLG) system can be misunderstood by the hearer, even when they are semantically correct. In an interactive setting, the NLG system can try to recognize such misunderstandings and correct them. We present an algorithm for generating corrective RE... | P17-1063 | 10.18653/v1/P17-1063 | null | null | null |
P17-1064 | Modeling Source Syntax for Neural Machine Translation | https://aclanthology.org/P17-1064/ | [
"Junhui Li",
"Deyi Xiong",
"Zhaopeng Tu",
"Muhua Zhu",
"Min Zhang",
"Guodong Zhou"
] | Even though a linguistics-free sequence to sequence model in neural machine translation (NMT) has certain capability of implicitly learning syntactic information of source sentences, this paper shows that source syntax can be explicitly incorporated into NMT effectively to provide further improvements. Specifically, we... | P17-1064 | 10.18653/v1/P17-1064 | null | 1705.01020 | title_snapshot |
P17-1065 | Sequence-to-Dependency Neural Machine Translation | https://aclanthology.org/P17-1065/ | [
"Shuangzhi Wu",
"Dongdong Zhang",
"Nan Yang",
"Mu Li",
"Ming Zhou"
] | Nowadays a typical Neural Machine Translation (NMT) model generates translations from left to right as a linear sequence, during which latent syntactic structures of the target sentences are not explicitly concerned. Inspired by the success of using syntactic knowledge of target language for improving statistical machi... | P17-1065 | 10.18653/v1/P17-1065 | null | null | null |
P17-1066 | Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning | https://aclanthology.org/P17-1066/ | [
"Jing Ma",
"Wei Gao",
"Kam-Fai Wong"
] | How fake news goes viral via social media? How does its propagation pattern differ from real stories? In this paper, we attempt to address the problem of identifying rumors, i.e., fake information, out of microblog posts based on their propagation structure. We firstly model microblog posts diffusion with propagation t... | P17-1066 | 10.18653/v1/P17-1066 | null | null | null |
P17-1067 | EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks | https://aclanthology.org/P17-1067/ | [
"Muhammad Abdul-Mageed",
"Lyle Ungar"
] | Accurate detection of emotion from natural language has applications ranging from building emotional chatbots to better understanding individuals and their lives. However, progress on emotion detection has been hampered by the absence of large labeled datasets. In this work, we build a very large dataset for fine-grain... | P17-1067 | 10.18653/v1/P17-1067 | null | null | null |
P17-1068 | Beyond Binary Labels: Political Ideology Prediction of Twitter Users | https://aclanthology.org/P17-1068/ | [
"Daniel Preoţiuc-Pietro",
"Ye Liu",
"Daniel Hopkins",
"Lyle Ungar"
] | Automatic political orientation prediction from social media posts has to date proven successful only in distinguishing between publicly declared liberals and conservatives in the US. This study examines users’ political ideology using a seven-point scale which enables us to identify politically moderate and neutral us... | P17-1068 | 10.18653/v1/P17-1068 | null | null | null |
P17-1069 | Leveraging Behavioral and Social Information for Weakly Supervised Collective Classification of Political Discourse on Twitter | https://aclanthology.org/P17-1069/ | [
"Kristen Johnson",
"Di Jin",
"Dan Goldwasser"
] | Framing is a political strategy in which politicians carefully word their statements in order to control public perception of issues. Previous works exploring political framing typically analyze frame usage in longer texts, such as congressional speeches. We present a collection of weakly supervised models which harnes... | P17-1069 | 10.18653/v1/P17-1069 | null | null | null |
P17-1070 | A Nested Attention Neural Hybrid Model for Grammatical Error Correction | https://aclanthology.org/P17-1070/ | [
"Jianshu Ji",
"Qinlong Wang",
"Kristina Toutanova",
"Yongen Gong",
"Steven Truong",
"Jianfeng Gao"
] | Grammatical error correction (GEC) systems strive to correct both global errors inword order and usage, and local errors inspelling and inflection. Further developing upon recent work on neural machine translation, we propose a new hybrid neural model with nested attention layers for GEC.Experiments show that the new m... | P17-1070 | 10.18653/v1/P17-1070 | null | 1707.02026 | title_snapshot |
P17-1071 | TextFlow: A Text Similarity Measure based on Continuous Sequences | https://aclanthology.org/P17-1071/ | [
"Yassine Mrabet",
"Halil Kilicoglu",
"Dina Demner-Fushman"
] | Text similarity measures are used in multiple tasks such as plagiarism detection, information ranking and recognition of paraphrases and textual entailment. While recent advances in deep learning highlighted the relevance of sequential models in natural language generation, existing similarity measures do not fully exp... | P17-1071 | 10.18653/v1/P17-1071 | null | null | null |
P17-1072 | Friendships, Rivalries, and Trysts: Characterizing Relations between Ideas in Texts | https://aclanthology.org/P17-1072/ | [
"Chenhao Tan",
"Dallas Card",
"Noah A. Smith"
] | Understanding how ideas relate to each other is a fundamental question in many domains, ranging from intellectual history to public communication. Because ideas are naturally embedded in texts, we propose the first framework to systematically characterize the relations between ideas based on their occurrence in a corpu... | P17-1072 | 10.18653/v1/P17-1072 | null | 1704.07828 | title_snapshot |
P17-1073 | Polish evaluation dataset for compositional distributional semantics models | https://aclanthology.org/P17-1073/ | [
"Alina Wróblewska",
"Katarzyna Krasnowska-Kieraś"
] | The paper presents a procedure of building an evaluation dataset. for the validation of compositional distributional semantics models estimated for languages other than English. The procedure generally builds on steps designed to assemble the SICK corpus, which contains pairs of English sentences annotated for semantic... | P17-1073 | 10.18653/v1/P17-1073 | null | null | null |
P17-1074 | Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction | https://aclanthology.org/P17-1074/ | [
"Christopher Bryant",
"Mariano Felice",
"Ted Briscoe"
] | Until now, error type performance for Grammatical Error Correction (GEC) systems could only be measured in terms of recall because system output is not annotated. To overcome this problem, we introduce ERRANT, a grammatical ERRor ANnotation Toolkit designed to automatically extract edits from parallel original and corr... | P17-1074 | 10.18653/v1/P17-1074 | null | null | null |
P17-1075 | Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability | https://aclanthology.org/P17-1075/ | [
"Saku Sugawara",
"Yusuke Kido",
"Hikaru Yokono",
"Akiko Aizawa"
] | Knowing the quality of reading comprehension (RC) datasets is important for the development of natural-language understanding systems. In this study, two classes of metrics were adopted for evaluating RC datasets: prerequisite skills and readability. We applied these classes to six existing datasets, including MCTest a... | P17-1075 | 10.18653/v1/P17-1075 | null | null | null |
P17-1076 | A Minimal Span-Based Neural Constituency Parser | https://aclanthology.org/P17-1076/ | [
"Mitchell Stern",
"Jacob Andreas",
"Dan Klein"
] | In this work, we present a minimal neural model for constituency parsing based on independent scoring of labels and spans. We show that this model is not only compatible with classical dynamic programming techniques, but also admits a novel greedy top-down inference algorithm based on recursive partitioning of the inpu... | P17-1076 | 10.18653/v1/P17-1076 | null | 1705.03919 | title_snapshot |
P17-1077 | Semantic Dependency Parsing via Book Embedding | https://aclanthology.org/P17-1077/ | [
"Weiwei Sun",
"Junjie Cao",
"Xiaojun Wan"
] | We model a dependency graph as a book, a particular kind of topological space, for semantic dependency parsing. The spine of the book is made up of a sequence of words, and each page contains a subset of noncrossing arcs. To build a semantic graph for a given sentence, we design new Maximum Subgraph algorithms to gener... | P17-1077 | 10.18653/v1/P17-1077 | null | null | null |
P17-1078 | Neural Word Segmentation with Rich Pretraining | https://aclanthology.org/P17-1078/ | [
"Jie Yang",
"Yue Zhang",
"Fei Dong"
] | Neural word segmentation research has benefited from large-scale raw texts by leveraging them for pretraining character and word embeddings. On the other hand, statistical segmentation research has exploited richer sources of external information, such as punctuation, automatic segmentation and POS. We investigate the ... | P17-1078 | 10.18653/v1/P17-1078 | null | 1704.08960 | title_snapshot |
P17-1079 | Neural Machine Translation via Binary Code Prediction | https://aclanthology.org/P17-1079/ | [
"Yusuke Oda",
"Philip Arthur",
"Graham Neubig",
"Koichiro Yoshino",
"Satoshi Nakamura"
] | In this paper, we propose a new method for calculating the output layer in neural machine translation systems. The method is based on predicting a binary code for each word and can reduce computation time/memory requirements of the output layer to be logarithmic in vocabulary size in the best case. In addition, we also... | P17-1079 | 10.18653/v1/P17-1079 | null | 1704.06918 | title_snapshot |
P17-1080 | What do Neural Machine Translation Models Learn about Morphology? | https://aclanthology.org/P17-1080/ | [
"Yonatan Belinkov",
"Nadir Durrani",
"Fahim Dalvi",
"Hassan Sajjad",
"James Glass"
] | Neural machine translation (MT) models obtain state-of-the-art performance while maintaining a simple, end-to-end architecture. However, little is known about what these models learn about source and target languages during the training process. In this work, we analyze the representations learned by neural MT models a... | P17-1080 | 10.18653/v1/P17-1080 | null | 1704.03471 | title_snapshot |
P17-1081 | Context-Dependent Sentiment Analysis in User-Generated Videos | https://aclanthology.org/P17-1081/ | [
"Soujanya Poria",
"Erik Cambria",
"Devamanyu Hazarika",
"Navonil Majumder",
"Amir Zadeh",
"Louis-Philippe Morency"
] | Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos. Current research considers utterances as independent entities, i.e., ignores the interdependencies and relations among the utterances of a video. In this paper, we propose a LSTM-based model that e... | P17-1081 | 10.18653/v1/P17-1081 | null | null | null |
P17-1082 | A Multidimensional Lexicon for Interpersonal Stancetaking | https://aclanthology.org/P17-1082/ | [
"Umashanthi Pavalanathan",
"Jim Fitzpatrick",
"Scott Kiesling",
"Jacob Eisenstein"
] | The sociolinguistic construct of stancetaking describes the activities through which discourse participants create and signal relationships to their interlocutors, to the topic of discussion, and to the talk itself. Stancetaking underlies a wide range of interactional phenomena, relating to formality, politeness, affec... | P17-1082 | 10.18653/v1/P17-1082 | null | null | null |
P17-1083 | Tandem Anchoring: a Multiword Anchor Approach for Interactive Topic Modeling | https://aclanthology.org/P17-1083/ | [
"Jeffrey Lund",
"Connor Cook",
"Kevin Seppi",
"Jordan Boyd-Graber"
] | Interactive topic models are powerful tools for those seeking to understand large collections of text. However, existing sampling-based interactive topic modeling approaches scale poorly to large data sets. Anchor methods, which use a single word to uniquely identify a topic, offer the speed needed for interactive work... | P17-1083 | 10.18653/v1/P17-1083 | null | null | null |
P17-1084 | Apples to Apples: Learning Semantics of Common Entities Through a Novel Comprehension Task | https://aclanthology.org/P17-1084/ | [
"Omid Bakhshandeh",
"James Allen"
] | Understanding common entities and their attributes is a primary requirement for any system that comprehends natural language. In order to enable learning about common entities, we introduce a novel machine comprehension task, GuessTwo: given a short paragraph comparing different aspects of two real-world semantically-s... | P17-1084 | 10.18653/v1/P17-1084 | null | null | null |
P17-1085 | Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees | https://aclanthology.org/P17-1085/ | [
"Arzoo Katiyar",
"Claire Cardie"
] | We present a novel attention-based recurrent neural network for joint extraction of entity mentions and relations. We show that attention along with long short term memory (LSTM) network can extract semantic relations between entity mentions without having access to dependency trees. Experiments on Automatic Content Ex... | P17-1085 | 10.18653/v1/P17-1085 | null | null | null |
P17-1086 | Naturalizing a Programming Language via Interactive Learning | https://aclanthology.org/P17-1086/ | [
"Sida I. Wang",
"Samuel Ginn",
"Percy Liang",
"Christopher D. Manning"
] | Our goal is to create a convenient natural language interface for performing well-specified but complex actions such as analyzing data, manipulating text, and querying databases. However, existing natural language interfaces for such tasks are quite primitive compared to the power one wields with a programming language... | P17-1086 | 10.18653/v1/P17-1086 | null | 1704.06956 | title_snapshot |
P17-1087 | Semantic Word Clusters Using Signed Spectral Clustering | https://aclanthology.org/P17-1087/ | [
"João Sedoc",
"Jean Gallier",
"Dean Foster",
"Lyle Ungar"
] | Vector space representations of words capture many aspects of word similarity, but such methods tend to produce vector spaces in which antonyms (as well as synonyms) are close to each other. For spectral clustering using such word embeddings, words are points in a vector space where synonyms are linked with positive we... | P17-1087 | 10.18653/v1/P17-1087 | null | 1601.05403 | title_judge |
P17-1088 | An Interpretable Knowledge Transfer Model for Knowledge Base Completion | https://aclanthology.org/P17-1088/ | [
"Qizhe Xie",
"Xuezhe Ma",
"Zihang Dai",
"Eduard Hovy"
] | Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness. We propose a novel embedding model, ITransF, to perform knowledge base completion. Equipped with a sparse attention mechanism, ITransF discovers hidden concepts of relations and transfer statistica... | P17-1088 | 10.18653/v1/P17-1088 | null | 1704.05908 | title_snapshot |
P17-1089 | Learning a Neural Semantic Parser from User Feedback | https://aclanthology.org/P17-1089/ | [
"Srinivasan Iyer",
"Ioannis Konstas",
"Alvin Cheung",
"Jayant Krishnamurthy",
"Luke Zettlemoyer"
] | We present an approach to rapidly and easily build natural language interfaces to databases for new domains, whose performance improves over time based on user feedback, and requires minimal intervention. To achieve this, we adapt neural sequence models to map utterances directly to SQL with its full expressivity, bypa... | P17-1089 | 10.18653/v1/P17-1089 | null | 1704.08760 | title_snapshot |
P17-1090 | Joint Modeling of Content and Discourse Relations in Dialogues | https://aclanthology.org/P17-1090/ | [
"Kechen Qin",
"Lu Wang",
"Joseph Kim"
] | We present a joint modeling approach to identify salient discussion points in spoken meetings as well as to label the discourse relations between speaker turns. A variation of our model is also discussed when discourse relations are treated as latent variables. Experimental results on two popular meeting corpora show t... | P17-1090 | 10.18653/v1/P17-1090 | null | 1705.05039 | title_snapshot |
P17-1091 | Argument Mining with Structured SVMs and RNNs | https://aclanthology.org/P17-1091/ | [
"Vlad Niculae",
"Joonsuk Park",
"Claire Cardie"
] | We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is the case in over 20% of the web comments dataset we release.) Our model jointly learns elementary unit type classification and argumentat... | P17-1091 | 10.18653/v1/P17-1091 | null | 1704.06869 | title_snapshot |
P17-1092 | Neural Discourse Structure for Text Categorization | https://aclanthology.org/P17-1092/ | [
"Yangfeng Ji",
"Noah A. Smith"
] | We show that discourse structure, as defined by Rhetorical Structure Theory and provided by an existing discourse parser, benefits text categorization. Our approach uses a recursive neural network and a newly proposed attention mechanism to compute a representation of the text that focuses on salient content, from the ... | P17-1092 | 10.18653/v1/P17-1092 | null | 1702.01829 | title_snapshot |
P17-1093 | Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification | https://aclanthology.org/P17-1093/ | [
"Lianhui Qin",
"Zhisong Zhang",
"Hai Zhao",
"Zhiting Hu",
"Eric Xing"
] | Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve the recognition. We propose a feature imitation framework in which an implicit relation network is driven to learn from another ... | P17-1093 | 10.18653/v1/P17-1093 | null | 1704.00217 | title_snapshot |
P17-1094 | Don’t understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures | https://aclanthology.org/P17-1094/ | [
"Iryna Haponchyk",
"Alessandro Moschitti"
] | An interesting aspect of structured prediction is the evaluation of an output structure against the gold standard. Especially in the loss-augmented setting, the need of finding the max-violating constraint has severely limited the expressivity of effective loss functions. In this paper, we trade off exact computation f... | P17-1094 | 10.18653/v1/P17-1094 | null | null | null |
P17-1095 | Bayesian Modeling of Lexical Resources for Low-Resource Settings | https://aclanthology.org/P17-1095/ | [
"Nicholas Andrews",
"Mark Dredze",
"Benjamin Van Durme",
"Jason Eisner"
] | Lexical resources such as dictionaries and gazetteers are often used as auxiliary data for tasks such as part-of-speech induction and named-entity recognition. However, discriminative training with lexical features requires annotated data to reliably estimate the lexical feature weights and may result in overfitting th... | P17-1095 | 10.18653/v1/P17-1095 | null | null | null |
P17-1096 | Semi-Supervised QA with Generative Domain-Adaptive Nets | https://aclanthology.org/P17-1096/ | [
"Zhilin Yang",
"Junjie Hu",
"Ruslan Salakhutdinov",
"William Cohen"
] | We study the problem of semi-supervised question answering—utilizing unlabeled text to boost the performance of question answering models. We propose a novel training framework, the Generative Domain-Adaptive Nets. In this framework, we train a generative model to generate questions based on the unlabeled text, and com... | P17-1096 | 10.18653/v1/P17-1096 | null | 1702.02206 | title_snapshot |
P17-1097 | From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood | https://aclanthology.org/P17-1097/ | [
"Kelvin Guu",
"Panupong Pasupat",
"Evan Liu",
"Percy Liang"
] | Our goal is to learn a semantic parser that maps natural language utterances into executable programs when only indirect supervision is available: examples are labeled with the correct execution result, but not the program itself. Consequently, we must search the space of programs for those that output the correct resu... | P17-1097 | 10.18653/v1/P17-1097 | null | 1704.07926 | title_snapshot |
P17-1098 | Diversity driven attention model for query-based abstractive summarization | https://aclanthology.org/P17-1098/ | [
"Preksha Nema",
"Mitesh M. Khapra",
"Anirban Laha",
"Balaraman Ravindran"
] | Abstractive summarization aims to generate a shorter version of the document covering all the salient points in a compact and coherent fashion. On the other hand, query-based summarization highlights those points that are relevant in the context of a given query. The encode-attend-decode paradigm has achieved notable s... | P17-1098 | 10.18653/v1/P17-1098 | null | 1704.08300 | title_snapshot |
P17-1099 | Get To The Point: Summarization with Pointer-Generator Networks | https://aclanthology.org/P17-1099/ | [
"Abigail See",
"Peter J. Liu",
"Christopher D. Manning"
] | Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and the... | P17-1099 | 10.18653/v1/P17-1099 | null | 1704.04368 | title_snapshot |
P17-1100 | Supervised Learning of Automatic Pyramid for Optimization-Based Multi-Document Summarization | https://aclanthology.org/P17-1100/ | [
"Maxime Peyrard",
"Judith Eckle-Kohler"
] | We present a new supervised framework that learns to estimate automatic Pyramid scores and uses them for optimization-based extractive multi-document summarization. For learning automatic Pyramid scores, we developed a method for automatic training data generation which is based on a genetic algorithm using automatic P... | P17-1100 | 10.18653/v1/P17-1100 | null | null | null |
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