EMNLP
Collection
Accepted papers for EMNLP (Conference on Empirical Methods in Natural Language Processing), one dataset per year. • 13 items • Updated
paper_id stringlengths 8 8 | title stringlengths 18 145 | paper_url stringlengths 34 34 | authors listlengths 1 8 | abstract large_stringlengths 318 1.48k | 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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D17-1001 | Monolingual Phrase Alignment on Parse Forests | https://aclanthology.org/D17-1001/ | [
"Yuki Arase",
"Junichi Tsujii"
] | We propose an efficient method to conduct phrase alignment on parse forests for paraphrase detection. Unlike previous studies, our method identifies syntactic paraphrases under linguistically motivated grammar. In addition, it allows phrases to non-compositionally align to handle paraphrases with non-homographic phrase... | D17-1001 | 10.18653/v1/D17-1001 | null | null | null |
D17-1002 | Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set | https://aclanthology.org/D17-1002/ | [
"Tianze Shi",
"Liang Huang",
"Lillian Lee"
] | We first present a minimal feature set for transition-based dependency parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a) and Cross and Huang (2016a) of using bi-directional LSTM features. We plug our minimal feature set into the dynamic-programming framework of Huang and Sagae (2010) and Ku... | D17-1002 | 10.18653/v1/D17-1002 | null | 1708.09403 | title_snapshot |
D17-1003 | Quasi-Second-Order Parsing for 1-Endpoint-Crossing, Pagenumber-2 Graphs | https://aclanthology.org/D17-1003/ | [
"Junjie Cao",
"Sheng Huang",
"Weiwei Sun",
"Xiaojun Wan"
] | We propose a new Maximum Subgraph algorithm for first-order parsing to 1-endpoint-crossing, pagenumber-2 graphs. Our algorithm has two characteristics: (1) it separates the construction for noncrossing edges and crossing edges; (2) in a single construction step, whether to create a new arc is deterministic. These two c... | D17-1003 | 10.18653/v1/D17-1003 | null | null | null |
D17-1004 | Position-aware Attention and Supervised Data Improve Slot Filling | https://aclanthology.org/D17-1004/ | [
"Yuhao Zhang",
"Victor Zhong",
"Danqi Chen",
"Gabor Angeli",
"Christopher D. Manning"
] | Organized relational knowledge in the form of “knowledge graphs” is important for many applications. However, the ability to populate knowledge bases with facts automatically extracted from documents has improved frustratingly slowly. This paper simultaneously addresses two issues that have held back prior work. We fir... | D17-1004 | 10.18653/v1/D17-1004 | null | null | null |
D17-1005 | Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach | https://aclanthology.org/D17-1005/ | [
"Liyuan Liu",
"Xiang Ren",
"Qi Zhu",
"Shi Zhi",
"Huan Gui",
"Heng Ji",
"Jiawei Han"
] | Relation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-consuming. To overcome this drawback, we propose a novel framework, REHession, to conduct relation extractor learning using annotations from h... | D17-1005 | 10.18653/v1/D17-1005 | null | 1707.00166 | title_snapshot |
D17-1006 | Integrating Order Information and Event Relation for Script Event Prediction | https://aclanthology.org/D17-1006/ | [
"Zhongqing Wang",
"Yue Zhang",
"Ching-Yun Chang"
] | There has been a recent line of work automatically learning scripts from unstructured texts, by modeling narrative event chains. While the dominant approach group events using event pair relations, LSTMs have been used to encode full chains of narrative events. The latter has the advantage of learning long-range tempor... | D17-1006 | 10.18653/v1/D17-1006 | null | null | null |
D17-1007 | Entity Linking for Queries by Searching Wikipedia Sentences | https://aclanthology.org/D17-1007/ | [
"Chuanqi Tan",
"Furu Wei",
"Pengjie Ren",
"Weifeng Lv",
"Ming Zhou"
] | We present a simple yet effective approach for linking entities in queries. The key idea is to search sentences similar to a query from Wikipedia articles and directly use the human-annotated entities in the similar sentences as candidate entities for the query. Then, we employ a rich set of features, such as link-prob... | D17-1007 | 10.18653/v1/D17-1007 | null | 1704.02788 | title_snapshot |
D17-1008 | Train-O-Matic: Large-Scale Supervised Word Sense Disambiguation in Multiple Languages without Manual Training Data | https://aclanthology.org/D17-1008/ | [
"Tommaso Pasini",
"Roberto Navigli"
] | Annotating large numbers of sentences with senses is the heaviest requirement of current Word Sense Disambiguation. We present Train-O-Matic, a language-independent method for generating millions of sense-annotated training instances for virtually all meanings of words in a language’s vocabulary. The approach is fully ... | D17-1008 | 10.18653/v1/D17-1008 | null | null | null |
D17-1009 | Universal Semantic Parsing | https://aclanthology.org/D17-1009/ | [
"Siva Reddy",
"Oscar Täckström",
"Slav Petrov",
"Mark Steedman",
"Mirella Lapata"
] | Universal Dependencies (UD) offer a uniform cross-lingual syntactic representation, with the aim of advancing multilingual applications. Recent work shows that semantic parsing can be accomplished by transforming syntactic dependencies to logical forms. However, this work is limited to English, and cannot process depen... | D17-1009 | 10.18653/v1/D17-1009 | null | 1702.03196 | title_snapshot |
D17-1010 | Mimicking Word Embeddings using Subword RNNs | https://aclanthology.org/D17-1010/ | [
"Yuval Pinter",
"Robert Guthrie",
"Jacob Eisenstein"
] | Word embeddings improve generalization over lexical features by placing each word in a lower-dimensional space, using distributional information obtained from unlabeled data. However, the effectiveness of word embeddings for downstream NLP tasks is limited by out-of-vocabulary (OOV) words, for which embeddings do not e... | D17-1010 | 10.18653/v1/D17-1010 | null | 1707.06961 | title_snapshot |
D17-1011 | Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languages | https://aclanthology.org/D17-1011/ | [
"Ehsaneddin Asgari",
"Hinrich Schütze"
] | We present SuperPivot, an analysis method for low-resource languages that occur in a superparallel corpus, i.e., in a corpus that contains an order of magnitude more languages than parallel corpora currently in use. We show that SuperPivot performs well for the crosslingual analysis of the linguistic phenomenon of tens... | D17-1011 | 10.18653/v1/D17-1011 | null | 1704.08914 | title_snapshot |
D17-1012 | Neural Machine Translation with Source-Side Latent Graph Parsing | https://aclanthology.org/D17-1012/ | [
"Kazuma Hashimoto",
"Yoshimasa Tsuruoka"
] | This paper presents a novel neural machine translation model which jointly learns translation and source-side latent graph representations of sentences. Unlike existing pipelined approaches using syntactic parsers, our end-to-end model learns a latent graph parser as part of the encoder of an attention-based neural mac... | D17-1012 | 10.18653/v1/D17-1012 | null | 1702.02265 | title_snapshot |
D17-1013 | Neural Machine Translation with Word Predictions | https://aclanthology.org/D17-1013/ | [
"Rongxiang Weng",
"Shujian Huang",
"Zaixiang Zheng",
"Xinyu Dai",
"Jiajun Chen"
] | In the encoder-decoder architecture for neural machine translation (NMT), the hidden states of the recurrent structures in the encoder and decoder carry the crucial information about the sentence. These vectors are generated by parameters which are updated by back-propagation of translation errors through time. We argu... | D17-1013 | 10.18653/v1/D17-1013 | null | 1708.01771 | title_snapshot |
D17-1014 | Towards Decoding as Continuous Optimisation in Neural Machine Translation | https://aclanthology.org/D17-1014/ | [
"Cong Duy Vu Hoang",
"Gholamreza Haffari",
"Trevor Cohn"
] | We propose a novel decoding approach for neural machine translation (NMT) based on continuous optimisation. We reformulate decoding, a discrete optimization problem, into a continuous problem, such that optimization can make use of efficient gradient-based techniques. Our powerful decoding framework allows for more acc... | D17-1014 | 10.18653/v1/D17-1014 | null | null | null |
D17-1015 | Where is Misty? Interpreting Spatial Descriptors by Modeling Regions in Space | https://aclanthology.org/D17-1015/ | [
"Nikita Kitaev",
"Dan Klein"
] | We present a model for locating regions in space based on natural language descriptions. Starting with a 3D scene and a sentence, our model is able to associate words in the sentence with regions in the scene, interpret relations such as ‘on top of’ or ‘next to,’ and finally locate the region described in the sentence.... | D17-1015 | 10.18653/v1/D17-1015 | null | null | null |
D17-1016 | Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks | https://aclanthology.org/D17-1016/ | [
"Afshin Rahimi",
"Timothy Baldwin",
"Trevor Cohn"
] | We propose a method for embedding two-dimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presenting two model variants for text-based geolocation and lexical dialectology. Evaluated over Twitter data, the proposed model outperforms con... | D17-1016 | 10.18653/v1/D17-1016 | null | 1708.04358 | title_snapshot |
D17-1017 | Obj2Text: Generating Visually Descriptive Language from Object Layouts | https://aclanthology.org/D17-1017/ | [
"Xuwang Yin",
"Vicente Ordonez"
] | Generating captions for images is a task that has recently received considerable attention. Another type of visual inputs are abstract scenes or object layouts where the only information provided is a set of objects and their locations. This type of imagery is commonly found in many applications in computer graphics, v... | D17-1017 | 10.18653/v1/D17-1017 | null | 1707.07102 | title_snapshot |
D17-1018 | End-to-end Neural Coreference Resolution | https://aclanthology.org/D17-1018/ | [
"Kenton Lee",
"Luheng He",
"Mike Lewis",
"Luke Zettlemoyer"
] | We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or hand-engineered mention detector. The key idea is to directly consider all spans in a document as potential mentions and learn distributions over possible antec... | D17-1018 | 10.18653/v1/D17-1018 | null | 1707.07045 | title_snapshot |
D17-1019 | Neural Net Models of Open-domain Discourse Coherence | https://aclanthology.org/D17-1019/ | [
"Jiwei Li",
"Dan Jurafsky"
] | Discourse coherence is strongly associated with text quality, making it important to natural language generation and understanding. Yet existing models of coherence focus on measuring individual aspects of coherence (lexical overlap, rhetorical structure, entity centering) in narrow domains. In this paper, we describe ... | D17-1019 | 10.18653/v1/D17-1019 | null | 1606.01545 | title_judge |
D17-1020 | Affinity-Preserving Random Walk for Multi-Document Summarization | https://aclanthology.org/D17-1020/ | [
"Kexiang Wang",
"Tianyu Liu",
"Zhifang Sui",
"Baobao Chang"
] | Multi-document summarization provides users with a short text that summarizes the information in a set of related documents. This paper introduces affinity-preserving random walk to the summarization task, which preserves the affinity relations of sentences by an absorbing random walk model. Meanwhile, we put forward a... | D17-1020 | 10.18653/v1/D17-1020 | null | null | null |
D17-1021 | A Mention-Ranking Model for Abstract Anaphora Resolution | https://aclanthology.org/D17-1021/ | [
"Ana Marasović",
"Leo Born",
"Juri Opitz",
"Anette Frank"
] | Resolving abstract anaphora is an important, but difficult task for text understanding. Yet, with recent advances in representation learning this task becomes a more tangible aim. A central property of abstract anaphora is that it establishes a relation between the anaphor embedded in the anaphoric sentence and its (ty... | D17-1021 | 10.18653/v1/D17-1021 | null | 1706.02256 | title_snapshot |
D17-1022 | Hierarchical Embeddings for Hypernymy Detection and Directionality | https://aclanthology.org/D17-1022/ | [
"Kim Anh Nguyen",
"Maximilian Köper",
"Sabine Schulte im Walde",
"Ngoc Thang Vu"
] | We present a novel neural model HyperVec to learn hierarchical embeddings for hypernymy detection and directionality. While previous embeddings have shown limitations on prototypical hypernyms, HyperVec represents an unsupervised measure where embeddings are learned in a specific order and capture the hypernym–hyponym ... | D17-1022 | 10.18653/v1/D17-1022 | null | 1707.07273 | title_snapshot |
D17-1023 | Ngram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics | https://aclanthology.org/D17-1023/ | [
"Zhe Zhao",
"Tao Liu",
"Shen Li",
"Bofang Li",
"Xiaoyong Du"
] | The existing word representation methods mostly limit their information source to word co-occurrence statistics. In this paper, we introduce ngrams into four representation methods: SGNS, GloVe, PPMI matrix, and its SVD factorization. Comprehensive experiments are conducted on word analogy and similarity tasks. The res... | D17-1023 | 10.18653/v1/D17-1023 | null | null | null |
D17-1024 | Dict2vec : Learning Word Embeddings using Lexical Dictionaries | https://aclanthology.org/D17-1024/ | [
"Julien Tissier",
"Christophe Gravier",
"Amaury Habrard"
] | Learning word embeddings on large unlabeled corpus has been shown to be successful in improving many natural language tasks. The most efficient and popular approaches learn or retrofit such representations using additional external data. Resulting embeddings are generally better than their corpus-only counterparts, alt... | D17-1024 | 10.18653/v1/D17-1024 | null | null | null |
D17-1025 | Learning Chinese Word Representations From Glyphs Of Characters | https://aclanthology.org/D17-1025/ | [
"Tzu-Ray Su",
"Hung-Yi Lee"
] | In this paper, we propose new methods to learn Chinese word representations. Chinese characters are composed of graphical components, which carry rich semantics. It is common for a Chinese learner to comprehend the meaning of a word from these graphical components. As a result, we propose models that enhance word repre... | D17-1025 | 10.18653/v1/D17-1025 | null | 1708.04755 | title_snapshot |
D17-1026 | Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext | https://aclanthology.org/D17-1026/ | [
"John Wieting",
"Jonathan Mallinson",
"Kevin Gimpel"
] | We consider the problem of learning general-purpose, paraphrastic sentence embeddings in the setting of Wieting et al. (2016b). We use neural machine translation to generate sentential paraphrases via back-translation of bilingual sentence pairs. We evaluate the paraphrase pairs by their ability to serve as training da... | D17-1026 | 10.18653/v1/D17-1026 | null | 1706.01847 | title_snapshot |
D17-1027 | Joint Embeddings of Chinese Words, Characters, and Fine-grained Subcharacter Components | https://aclanthology.org/D17-1027/ | [
"Jinxing Yu",
"Xun Jian",
"Hao Xin",
"Yangqiu Song"
] | Word embeddings have attracted much attention recently. Different from alphabetic writing systems, Chinese characters are often composed of subcharacter components which are also semantically informative. In this work, we propose an approach to jointly embed Chinese words as well as their characters and fine-grained su... | D17-1027 | 10.18653/v1/D17-1027 | null | null | null |
D17-1028 | Exploiting Morphological Regularities in Distributional Word Representations | https://aclanthology.org/D17-1028/ | [
"Arihant Gupta",
"Syed Sarfaraz Akhtar",
"Avijit Vajpayee",
"Arjit Srivastava",
"Madan Gopal Jhanwar",
"Manish Shrivastava"
] | We present an unsupervised, language agnostic approach for exploiting morphological regularities present in high dimensional vector spaces. We propose a novel method for generating embeddings of words from their morphological variants using morphological transformation operators. We evaluate this approach on MSR word a... | D17-1028 | 10.18653/v1/D17-1028 | null | null | null |
D17-1029 | Exploiting Word Internal Structures for Generic Chinese Sentence Representation | https://aclanthology.org/D17-1029/ | [
"Shaonan Wang",
"Jiajun Zhang",
"Chengqing Zong"
] | We introduce a novel mixed characterword architecture to improve Chinese sentence representations, by utilizing rich semantic information of word internal structures. Our architecture uses two key strategies. The first is a mask gate on characters, learning the relation among characters in a word. The second is a maxpo... | D17-1029 | 10.18653/v1/D17-1029 | null | null | null |
D17-1030 | High-risk learning: acquiring new word vectors from tiny data | https://aclanthology.org/D17-1030/ | [
"Aurélie Herbelot",
"Marco Baroni"
] | Distributional semantics models are known to struggle with small data. It is generally accepted that in order to learn ‘a good vector’ for a word, a model must have sufficient examples of its usage. This contradicts the fact that humans can guess the meaning of a word from a few occurrences only. In this paper, we show... | D17-1030 | 10.18653/v1/D17-1030 | null | 1707.06556 | title_snapshot |
D17-1031 | Word Embeddings based on Fixed-Size Ordinally Forgetting Encoding | https://aclanthology.org/D17-1031/ | [
"Joseph Sanu",
"Mingbin Xu",
"Hui Jiang",
"Quan Liu"
] | In this paper, we propose to learn word embeddings based on the recent fixed-size ordinally forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence into a fixed-size representation. We use FOFE to fully encode the left and right context of each word in a corpus to construct a no... | D17-1031 | 10.18653/v1/D17-1031 | null | null | null |
D17-1032 | VecShare: A Framework for Sharing Word Representation Vectors | https://aclanthology.org/D17-1032/ | [
"Jared Fernandez",
"Zhaocheng Yu",
"Doug Downey"
] | Many Natural Language Processing (NLP) models rely on distributed vector representations of words. Because the process of training word vectors can require large amounts of data and computation, NLP researchers and practitioners often utilize pre-trained embeddings downloaded from the Web. However, finding the best emb... | D17-1032 | 10.18653/v1/D17-1032 | null | null | null |
D17-1033 | Word Re-Embedding via Manifold Dimensionality Retention | https://aclanthology.org/D17-1033/ | [
"Souleiman Hasan",
"Edward Curry"
] | Word embeddings seek to recover a Euclidean metric space by mapping words into vectors, starting from words co-occurrences in a corpus. Word embeddings may underestimate the similarity between nearby words, and overestimate it between distant words in the Euclidean metric space. In this paper, we re-embed pre-trained w... | D17-1033 | 10.18653/v1/D17-1033 | null | null | null |
D17-1034 | MUSE: Modularizing Unsupervised Sense Embeddings | https://aclanthology.org/D17-1034/ | [
"Guang-He Lee",
"Yun-Nung Chen"
] | This paper proposes to address the word sense ambiguity issue in an unsupervised manner, where word sense representations are learned along a word sense selection mechanism given contexts. Prior work focused on designing a single model to deliver both mechanisms, and thus suffered from either coarse-grained representat... | D17-1034 | 10.18653/v1/D17-1034 | null | 1704.04601 | title_snapshot |
D17-1035 | Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging | https://aclanthology.org/D17-1035/ | [
"Nils Reimers",
"Iryna Gurevych"
] | In this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches. We demonstrate for common sequence tagging tasks that the seed value for the random number generator can result in statistically significant (p < 10^{-4}) differences for state-of-the-art systems. Fo... | D17-1035 | 10.18653/v1/D17-1035 | null | 1707.09861 | title_snapshot |
D17-1036 | Learning What’s Easy: Fully Differentiable Neural Easy-First Taggers | https://aclanthology.org/D17-1036/ | [
"André F. T. Martins",
"Julia Kreutzer"
] | We introduce a novel neural easy-first decoder that learns to solve sequence tagging tasks in a flexible order. In contrast to previous easy-first decoders, our models are end-to-end differentiable. The decoder iteratively updates a “sketch” of the predictions over the sequence. At its core is an attention mechanism th... | D17-1036 | 10.18653/v1/D17-1036 | null | null | null |
D17-1037 | Incremental Skip-gram Model with Negative Sampling | https://aclanthology.org/D17-1037/ | [
"Nobuhiro Kaji",
"Hayato Kobayashi"
] | This paper explores an incremental training strategy for the skip-gram model with negative sampling (SGNS) from both empirical and theoretical perspectives. Existing methods of neural word embeddings, including SGNS, are multi-pass algorithms and thus cannot perform incremental model update. To address this problem, we... | D17-1037 | 10.18653/v1/D17-1037 | null | 1704.03956 | title_snapshot |
D17-1038 | Learning to select data for transfer learning with Bayesian Optimization | https://aclanthology.org/D17-1038/ | [
"Sebastian Ruder",
"Barbara Plank"
] | Domain similarity measures can be used to gauge adaptability and select suitable data for transfer learning, but existing approaches define ad hoc measures that are deemed suitable for respective tasks. Inspired by work on curriculum learning, we propose to learn data selection measures using Bayesian Optimization and ... | D17-1038 | 10.18653/v1/D17-1038 | null | 1707.05246 | title_snapshot |
D17-1039 | Unsupervised Pretraining for Sequence to Sequence Learning | https://aclanthology.org/D17-1039/ | [
"Prajit Ramachandran",
"Peter Liu",
"Quoc Le"
] | This work presents a general unsupervised learning method to improve the accuracy of sequence to sequence (seq2seq) models. In our method, the weights of the encoder and decoder of a seq2seq model are initialized with the pretrained weights of two language models and then fine-tuned with labeled data. We apply this met... | D17-1039 | 10.18653/v1/D17-1039 | null | 1611.02683 | title_snapshot |
D17-1040 | Efficient Attention using a Fixed-Size Memory Representation | https://aclanthology.org/D17-1040/ | [
"Denny Britz",
"Melody Guan",
"Minh-Thang Luong"
] | The standard content-based attention mechanism typically used in sequence-to-sequence models is computationally expensive as it requires the comparison of large encoder and decoder states at each time step. In this work, we propose an alternative attention mechanism based on a fixed size memory representation that is m... | D17-1040 | 10.18653/v1/D17-1040 | null | 1707.00110 | title_snapshot |
D17-1041 | Rotated Word Vector Representations and their Interpretability | https://aclanthology.org/D17-1041/ | [
"Sungjoon Park",
"JinYeong Bak",
"Alice Oh"
] | Vector representation of words improves performance in various NLP tasks, but the high dimensional word vectors are very difficult to interpret. We apply several rotation algorithms to the vector representation of words to improve the interpretability. Unlike previous approaches that induce sparsity, the rotated vector... | D17-1041 | 10.18653/v1/D17-1041 | null | null | null |
D17-1042 | A causal framework for explaining the predictions of black-box sequence-to-sequence models | https://aclanthology.org/D17-1042/ | [
"David Alvarez-Melis",
"Tommi Jaakkola"
] | We interpret the predictions of any black-box structured input-structured output model around a specific input-output pair. Our method returns an “explanation” consisting of groups of input-output tokens that are causally related. These dependencies are inferred by querying the model with perturbed inputs, generating a... | D17-1042 | 10.18653/v1/D17-1042 | null | 1707.01943 | title_snapshot |
D17-1043 | Piecewise Latent Variables for Neural Variational Text Processing | https://aclanthology.org/D17-1043/ | [
"Iulian Vlad Serban",
"Alexander G. Ororbia",
"Joelle Pineau",
"Aaron Courville"
] | Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variables, such as variational autoencoders. The hope is that such models will learn to represent rich, multi-modal latent factors in real-world data, such as natural language text. Howeve... | D17-1043 | 10.18653/v1/D17-1043 | null | 1612.00377 | title_snapshot |
D17-1044 | Learning the Structure of Variable-Order CRFs: a finite-state perspective | https://aclanthology.org/D17-1044/ | [
"Thomas Lavergne",
"François Yvon"
] | The computational complexity of linear-chain Conditional Random Fields (CRFs) makes it difficult to deal with very large label sets and long range dependencies. Such situations are not rare and arise when dealing with morphologically rich languages or joint labelling tasks. We extend here recent proposals to consider v... | D17-1044 | 10.18653/v1/D17-1044 | null | null | null |
D17-1045 | Sparse Communication for Distributed Gradient Descent | https://aclanthology.org/D17-1045/ | [
"Alham Fikri Aji",
"Kenneth Heafield"
] | We make distributed stochastic gradient descent faster by exchanging sparse updates instead of dense updates. Gradient updates are positively skewed as most updates are near zero, so we map the 99% smallest updates (by absolute value) to zero then exchange sparse matrices. This method can be combined with quantization ... | D17-1045 | 10.18653/v1/D17-1045 | null | 1704.05021 | title_snapshot |
D17-1046 | Why ADAGRAD Fails for Online Topic Modeling | https://aclanthology.org/D17-1046/ | [
"You Lu",
"Jeffrey Lund",
"Jordan Boyd-Graber"
] | Online topic modeling, i.e., topic modeling with stochastic variational inference, is a powerful and efficient technique for analyzing large datasets, and ADAGRAD is a widely-used technique for tuning learning rates during online gradient optimization. However, these two techniques do not work well together. We show th... | D17-1046 | 10.18653/v1/D17-1046 | null | null | null |
D17-1047 | Recurrent Attention Network on Memory for Aspect Sentiment Analysis | https://aclanthology.org/D17-1047/ | [
"Peng Chen",
"Zhongqian Sun",
"Lidong Bing",
"Wei Yang"
] | We propose a novel framework based on neural networks to identify the sentiment of opinion targets in a comment/review. Our framework adopts multiple-attention mechanism to capture sentiment features separated by a long distance, so that it is more robust against irrelevant information. The results of multiple attentio... | D17-1047 | 10.18653/v1/D17-1047 | null | null | null |
D17-1048 | A Cognition Based Attention Model for Sentiment Analysis | https://aclanthology.org/D17-1048/ | [
"Yunfei Long",
"Qin Lu",
"Rong Xiang",
"Minglei Li",
"Chu-Ren Huang"
] | Attention models are proposed in sentiment analysis because some words are more important than others. However,most existing methods either use local context based text information or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. A reading... | D17-1048 | 10.18653/v1/D17-1048 | null | null | null |
D17-1049 | Author-aware Aspect Topic Sentiment Model to Retrieve Supporting Opinions from Reviews | https://aclanthology.org/D17-1049/ | [
"Lahari Poddar",
"Wynne Hsu",
"Mong Li Lee"
] | User generated content about products and services in the form of reviews are often diverse and even contradictory. This makes it difficult for users to know if an opinion in a review is prevalent or biased. We study the problem of searching for supporting opinions in the context of reviews. We propose a framework call... | D17-1049 | 10.18653/v1/D17-1049 | null | null | null |
D17-1050 | Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very Personal | https://aclanthology.org/D17-1050/ | [
"Aniruddha Ghosh",
"Tony Veale"
] | Sarcasm is a pervasive phenomenon in social media, permitting the concise communication of meaning, affect and attitude. Concision requires wit to produce and wit to understand, which demands from each party knowledge of norms, context and a speaker’s mindset. Insight into a speaker’s psychological profile at the time ... | D17-1050 | 10.18653/v1/D17-1050 | null | null | null |
D17-1051 | Identifying Humor in Reviews using Background Text Sources | https://aclanthology.org/D17-1051/ | [
"Alex Morales",
"Chengxiang Zhai"
] | We study the problem of automatically identifying humorous text from a new kind of text data, i.e., online reviews. We propose a generative language model, based on the theory of incongruity, to model humorous text, which allows us to leverage background text sources, such as Wikipedia entry descriptions, and enables c... | D17-1051 | 10.18653/v1/D17-1051 | null | null | null |
D17-1052 | Sentiment Lexicon Construction with Representation Learning Based on Hierarchical Sentiment Supervision | https://aclanthology.org/D17-1052/ | [
"Leyi Wang",
"Rui Xia"
] | Sentiment lexicon is an important tool for identifying the sentiment polarity of words and texts. How to automatically construct sentiment lexicons has become a research topic in the field of sentiment analysis and opinion mining. Recently there were some attempts to employ representation learning algorithms to constru... | D17-1052 | 10.18653/v1/D17-1052 | null | null | null |
D17-1053 | Towards a Universal Sentiment Classifier in Multiple languages | https://aclanthology.org/D17-1053/ | [
"Kui Xu",
"Xiaojun Wan"
] | Existing sentiment classifiers usually work for only one specific language, and different classification models are used in different languages. In this paper we aim to build a universal sentiment classifier with a single classification model in multiple different languages. In order to achieve this goal, we propose to... | D17-1053 | 10.18653/v1/D17-1053 | null | null | null |
D17-1054 | Capturing User and Product Information for Document Level Sentiment Analysis with Deep Memory Network | https://aclanthology.org/D17-1054/ | [
"Zi-Yi Dou"
] | Document-level sentiment classification is a fundamental problem which aims to predict a user’s overall sentiment about a product in a document. Several methods have been proposed to tackle the problem whereas most of them fail to consider the influence of users who express the sentiment and products which are evaluate... | D17-1054 | 10.18653/v1/D17-1054 | null | null | null |
D17-1055 | Identifying and Tracking Sentiments and Topics from Social Media Texts during Natural Disasters | https://aclanthology.org/D17-1055/ | [
"Min Yang",
"Jincheng Mei",
"Heng Ji",
"Wei Zhao",
"Zhou Zhao",
"Xiaojun Chen"
] | We study the problem of identifying the topics and sentiments and tracking their shifts from social media texts in different geographical regions during emergencies and disasters. We propose a location-based dynamic sentiment-topic model (LDST) which can jointly model topic, sentiment, time and Geolocation information.... | D17-1055 | 10.18653/v1/D17-1055 | null | null | null |
D17-1056 | Refining Word Embeddings for Sentiment Analysis | https://aclanthology.org/D17-1056/ | [
"Liang-Chih Yu",
"Jin Wang",
"K. Robert Lai",
"Xuejie Zhang"
] | Word embeddings that can capture semantic and syntactic information from contexts have been extensively used for various natural language processing tasks. However, existing methods for learning context-based word embeddings typically fail to capture sufficient sentiment information. This may result in words with simil... | D17-1056 | 10.18653/v1/D17-1056 | null | null | null |
D17-1057 | A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis | https://aclanthology.org/D17-1057/ | [
"Md Shad Akhtar",
"Abhishek Kumar",
"Deepanway Ghosal",
"Asif Ekbal",
"Pushpak Bhattacharyya"
] | In this paper, we propose a novel method for combining deep learning and classical feature based models using a Multi-Layer Perceptron (MLP) network for financial sentiment analysis. We develop various deep learning models based on Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and Gated Recurrent Un... | D17-1057 | 10.18653/v1/D17-1057 | null | null | null |
D17-1058 | Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings | https://aclanthology.org/D17-1058/ | [
"Raksha Sharma",
"Arpan Somani",
"Lakshya Kumar",
"Pushpak Bhattacharyya"
] | Identification of intensity ordering among polar (positive or negative) words which have the same semantics can lead to a fine-grained sentiment analysis. For example, ‘master’, ‘seasoned’ and ‘familiar’ point to different intensity levels, though they all convey the same meaning (semantics), i.e., expertise: having a ... | D17-1058 | 10.18653/v1/D17-1058 | null | null | null |
D17-1059 | Sentiment Lexicon Expansion Based on Neural PU Learning, Double Dictionary Lookup, and Polarity Association | https://aclanthology.org/D17-1059/ | [
"Yasheng Wang",
"Yang Zhang",
"Bing Liu"
] | Although many sentiment lexicons in different languages exist, most are not comprehensive. In a recent sentiment analysis application, we used a large Chinese sentiment lexicon and found that it missed a large number of sentiment words in social media. This prompted us to make a new attempt to study sentiment lexicon e... | D17-1059 | 10.18653/v1/D17-1059 | null | null | null |
D17-1060 | DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning | https://aclanthology.org/D17-1060/ | [
"Wenhan Xiong",
"Thien Hoang",
"William Yang Wang"
] | We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector-space by... | D17-1060 | 10.18653/v1/D17-1060 | null | 1707.06690 | title_snapshot |
D17-1061 | Task-Oriented Query Reformulation with Reinforcement Learning | https://aclanthology.org/D17-1061/ | [
"Rodrigo Nogueira",
"Kyunghyun Cho"
] | Search engines play an important role in our everyday lives by assisting us in finding the information we need. When we input a complex query, however, results are often far from satisfactory. In this work, we introduce a query reformulation system based on a neural network that rewrites a query to maximize the number ... | D17-1061 | 10.18653/v1/D17-1061 | null | 1704.04572 | title_snapshot |
D17-1062 | Sentence Simplification with Deep Reinforcement Learning | https://aclanthology.org/D17-1062/ | [
"Xingxing Zhang",
"Mirella Lapata"
] | Sentence simplification aims to make sentences easier to read and understand. Most recent approaches draw on insights from machine translation to learn simplification rewrites from monolingual corpora of complex and simple sentences. We address the simplification problem with an encoder-decoder model coupled with a dee... | D17-1062 | 10.18653/v1/D17-1062 | null | 1703.10931 | title_snapshot |
D17-1063 | Learning how to Active Learn: A Deep Reinforcement Learning Approach | https://aclanthology.org/D17-1063/ | [
"Meng Fang",
"Yuan Li",
"Trevor Cohn"
] | Active learning aims to select a small subset of data for annotation such that a classifier learned on the data is highly accurate. This is usually done using heuristic selection methods, however the effectiveness of such methods is limited and moreover, the performance of heuristics varies between datasets. To address... | D17-1063 | 10.18653/v1/D17-1063 | null | 1708.02383 | title_snapshot |
D17-1064 | Split and Rephrase | https://aclanthology.org/D17-1064/ | [
"Shashi Narayan",
"Claire Gardent",
"Shay B. Cohen",
"Anastasia Shimorina"
] | We propose a new sentence simplification task (Split-and-Rephrase) where the aim is to split a complex sentence into a meaning preserving sequence of shorter sentences. Like sentence simplification, splitting-and-rephrasing has the potential of benefiting both natural language processing and societal applications. Beca... | D17-1064 | 10.18653/v1/D17-1064 | null | 1707.06971 | title_snapshot |
D17-1065 | Neural Response Generation via GAN with an Approximate Embedding Layer | https://aclanthology.org/D17-1065/ | [
"Zhen Xu",
"Bingquan Liu",
"Baoxun Wang",
"Chengjie Sun",
"Xiaolong Wang",
"Zhuoran Wang",
"Chao Qi"
] | This paper presents a Generative Adversarial Network (GAN) to model single-turn short-text conversations, which trains a sequence-to-sequence (Seq2Seq) network for response generation simultaneously with a discriminative classifier that measures the differences between human-produced responses and machine-generated one... | D17-1065 | 10.18653/v1/D17-1065 | null | null | null |
D17-1066 | A Hybrid Convolutional Variational Autoencoder for Text Generation | https://aclanthology.org/D17-1066/ | [
"Stanislau Semeniuta",
"Aliaksei Severyn",
"Erhardt Barth"
] | In this paper we explore the effect of architectural choices on learning a variational autoencoder (VAE) for text generation. In contrast to the previously introduced VAE model for text where both the encoder and decoder are RNNs, we propose a novel hybrid architecture that blends fully feed-forward convolutional and d... | D17-1066 | 10.18653/v1/D17-1066 | null | 1702.02390 | title_snapshot |
D17-1067 | Filling the Blanks (hint: plural noun) for Mad Libs Humor | https://aclanthology.org/D17-1067/ | [
"Nabil Hossain",
"John Krumm",
"Lucy Vanderwende",
"Eric Horvitz",
"Henry Kautz"
] | Computerized generation of humor is a notoriously difficult AI problem. We develop an algorithm called Libitum that helps humans generate humor in a Mad Lib, which is a popular fill-in-the-blank game. The algorithm is based on a machine learned classifier that determines whether a potential fill-in word is funny in the... | D17-1067 | 10.18653/v1/D17-1067 | null | null | null |
D17-1068 | Measuring Thematic Fit with Distributional Feature Overlap | https://aclanthology.org/D17-1068/ | [
"Enrico Santus",
"Emmanuele Chersoni",
"Alessandro Lenci",
"Philippe Blache"
] | In this paper, we introduce a new distributional method for modeling predicate-argument thematic fit judgments. We use a syntax-based DSM to build a prototypical representation of verb-specific roles: for every verb, we extract the most salient second order contexts for each of its roles (i.e. the most salient dimensio... | D17-1068 | 10.18653/v1/D17-1068 | null | 1707.05967 | title_snapshot |
D17-1069 | SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations | https://aclanthology.org/D17-1069/ | [
"Dheeraj Mekala",
"Vivek Gupta",
"Bhargavi Paranjape",
"Harish Karnick"
] | We present a feature vector formation technique for documents - Sparse Composite Document Vector (SCDV) - which overcomes several shortcomings of the current distributional paragraph vector representations that are widely used for text representation. In SCDV, word embeddings are clustered to capture multiple semantic ... | D17-1069 | 10.18653/v1/D17-1069 | null | 1612.06778 | title_snapshot |
D17-1070 | Supervised Learning of Universal Sentence Representations from Natural Language Inference Data | https://aclanthology.org/D17-1070/ | [
"Alexis Conneau",
"Douwe Kiela",
"Holger Schwenk",
"Loïc Barrault",
"Antoine Bordes"
] | Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. Efforts to obtain embeddings for larger chunks of text, such as sentences, have however not been so successful. Several attempts at learning unsupervised representations of sentences have no... | D17-1070 | 10.18653/v1/D17-1070 | null | 1705.02364 | title_snapshot |
D17-1071 | Determining Semantic Textual Similarity using Natural Deduction Proofs | https://aclanthology.org/D17-1071/ | [
"Hitomi Yanaka",
"Koji Mineshima",
"Pascual Martínez-Gómez",
"Daisuke Bekki"
] | Determining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow information, capturing accurate semantics is difficult. By contrast, logical semantic representations capture deeper levels of sentence semantics, bu... | D17-1071 | 10.18653/v1/D17-1071 | null | 1707.08713 | title_snapshot |
D17-1072 | Multi-Grained Chinese Word Segmentation | https://aclanthology.org/D17-1072/ | [
"Chen Gong",
"Zhenghua Li",
"Min Zhang",
"Xinzhou Jiang"
] | Traditionally, word segmentation (WS) adopts the single-grained formalism, where a sentence corresponds to a single word sequence. However, Sproat et al. (1997) show that the inter-native-speaker consistency ratio over Chinese word boundaries is only 76%, indicating single-grained WS (SWS) imposes unnecessary challenge... | D17-1072 | 10.18653/v1/D17-1072 | null | null | null |
D17-1073 | Don’t Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic | https://aclanthology.org/D17-1073/ | [
"Nasser Zalmout",
"Nizar Habash"
] | This paper presents a model for Arabic morphological disambiguation based on Recurrent Neural Networks (RNN). We train Long Short-Term Memory (LSTM) cells in several configurations and embedding levels to model the various morphological features. Our experiments show that these models outperform state-of-the-art system... | D17-1073 | 10.18653/v1/D17-1073 | null | null | null |
D17-1074 | Paradigm Completion for Derivational Morphology | https://aclanthology.org/D17-1074/ | [
"Ryan Cotterell",
"Ekaterina Vylomova",
"Huda Khayrallah",
"Christo Kirov",
"David Yarowsky"
] | The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task. We overview the theoretical motivation for a paradigmatic treatment of derivational morphology, and introduce the task of derivational paradigm completion as a... | D17-1074 | 10.18653/v1/D17-1074 | null | 1708.09151 | title_snapshot |
D17-1075 | A Sub-Character Architecture for Korean Language Processing | https://aclanthology.org/D17-1075/ | [
"Karl Stratos"
] | We introduce a novel sub-character architecture that exploits a unique compositional structure of the Korean language. Our method decomposes each character into a small set of primitive phonetic units called jamo letters from which character- and word-level representations are induced. The jamo letters divulge syntacti... | D17-1075 | 10.18653/v1/D17-1075 | null | 1707.06341 | title_snapshot |
D17-1076 | Do LSTMs really work so well for PoS tagging? – A replication study | https://aclanthology.org/D17-1076/ | [
"Tobias Horsmann",
"Torsten Zesch"
] | A recent study by Plank et al. (2016) found that LSTM-based PoS taggers considerably improve over the current state-of-the-art when evaluated on the corpora of the Universal Dependencies project that use a coarse-grained tagset. We replicate this study using a fresh collection of 27 corpora of 21 languages that are ann... | D17-1076 | 10.18653/v1/D17-1076 | null | null | null |
D17-1077 | The Labeled Segmentation of Printed Books | https://aclanthology.org/D17-1077/ | [
"Lara McConnaughey",
"Jennifer Dai",
"David Bamman"
] | We introduce the task of book structure labeling: segmenting and assigning a fixed category (such as Table of Contents, Preface, Index) to the document structure of printed books. We manually annotate the page-level structural categories for a large dataset totaling 294,816 pages in 1,055 books evenly sampled from 1750... | D17-1077 | 10.18653/v1/D17-1077 | null | null | null |
D17-1078 | Cross-lingual Character-Level Neural Morphological Tagging | https://aclanthology.org/D17-1078/ | [
"Ryan Cotterell",
"Georg Heigold"
] | Even for common NLP tasks, sufficient supervision is not available in many languages – morphological tagging is no exception. In the work presented here, we explore a transfer learning scheme, whereby we train character-level recurrent neural taggers to predict morphological taggings for high-resource languages and low... | D17-1078 | 10.18653/v1/D17-1078 | null | 1708.09157 | title_snapshot |
D17-1079 | Word-Context Character Embeddings for Chinese Word Segmentation | https://aclanthology.org/D17-1079/ | [
"Hao Zhou",
"Zhenting Yu",
"Yue Zhang",
"Shujian Huang",
"Xinyu Dai",
"Jiajun Chen"
] | Neural parsers have benefited from automatically labeled data via dependency-context word embeddings. We investigate training character embeddings on a word-based context in a similar way, showing that the simple method improves state-of-the-art neural word segmentation models significantly, beating tri-training baseli... | D17-1079 | 10.18653/v1/D17-1079 | null | null | null |
D17-1080 | Segmentation-Free Word Embedding for Unsegmented Languages | https://aclanthology.org/D17-1080/ | [
"Takamasa Oshikiri"
] | In this paper, we propose a new pipeline of word embedding for unsegmented languages, called segmentation-free word embedding, which does not require word segmentation as a preprocessing step. Unlike space-delimited languages, unsegmented languages, such as Chinese and Japanese, require word segmentation as a preproces... | D17-1080 | 10.18653/v1/D17-1080 | null | null | null |
D17-1081 | From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems | https://aclanthology.org/D17-1081/ | [
"Mrinmaya Sachan",
"Kumar Dubey",
"Eric Xing"
] | Textbooks are rich sources of information. Harvesting structured knowledge from textbooks is a key challenge in many educational applications. As a case study, we present an approach for harvesting structured axiomatic knowledge from math textbooks. Our approach uses rich contextual and typographical features extracted... | D17-1081 | 10.18653/v1/D17-1081 | null | null | null |
D17-1082 | RACE: Large-scale ReAding Comprehension Dataset From Examinations | https://aclanthology.org/D17-1082/ | [
"Guokun Lai",
"Qizhe Xie",
"Hanxiao Liu",
"Yiming Yang",
"Eduard Hovy"
] | We present RACE, a new dataset for benchmark evaluation of methods in the reading comprehension task. Collected from the English exams for middle and high school Chinese students in the age range between 12 to 18, RACE consists of near 28,000 passages and near 100,000 questions generated by human experts (English instr... | D17-1082 | 10.18653/v1/D17-1082 | null | 1704.04683 | title_snapshot |
D17-1083 | Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers | https://aclanthology.org/D17-1083/ | [
"Mark Hopkins",
"Cristian Petrescu-Prahova",
"Roie Levin",
"Ronan Le Bras",
"Alvaro Herrasti",
"Vidur Joshi"
] | We present an approach for answering questions that span multiple sentences and exhibit sophisticated cross-sentence anaphoric phenomena, evaluating on a rich source of such questions – the math portion of the Scholastic Aptitude Test (SAT). By using a tree transducer cascade as its basic architecture, our system propa... | D17-1083 | 10.18653/v1/D17-1083 | null | null | null |
D17-1084 | Learning Fine-Grained Expressions to Solve Math Word Problems | https://aclanthology.org/D17-1084/ | [
"Danqing Huang",
"Shuming Shi",
"Chin-Yew Lin",
"Jian Yin"
] | This paper presents a novel template-based method to solve math word problems. This method learns the mappings between math concept phrases in math word problems and their math expressions from training data. For each equation template, we automatically construct a rich template sketch by aggregating information from v... | D17-1084 | 10.18653/v1/D17-1084 | null | null | null |
D17-1085 | Structural Embedding of Syntactic Trees for Machine Comprehension | https://aclanthology.org/D17-1085/ | [
"Rui Liu",
"Junjie Hu",
"Wei Wei",
"Zi Yang",
"Eric Nyberg"
] | Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees. In this paper, we propose structural embedding of syntactic trees (SEST), an algorithm framework t... | D17-1085 | 10.18653/v1/D17-1085 | null | 1703.00572 | title_snapshot |
D17-1086 | World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions | https://aclanthology.org/D17-1086/ | [
"Teng Long",
"Emmanuel Bengio",
"Ryan Lowe",
"Jackie Chi Kit Cheung",
"Doina Precup"
] | Humans interpret texts with respect to some background information, or world knowledge, and we would like to develop automatic reading comprehension systems that can do the same. In this paper, we introduce a task and several models to drive progress towards this goal. In particular, we propose the task of rare entity ... | D17-1086 | 10.18653/v1/D17-1086 | null | null | null |
D17-1087 | Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension | https://aclanthology.org/D17-1087/ | [
"David Golub",
"Po-Sen Huang",
"Xiaodong He",
"Li Deng"
] | We develop a technique for transfer learning in machine comprehension (MC) using a novel two-stage synthesis network. Given a high performing MC model in one domain, our technique aims to answer questions about documents in another domain, where we use no labeled data of question-answer pairs. Using the proposed synthe... | D17-1087 | 10.18653/v1/D17-1087 | null | 1706.09789 | title_snapshot |
D17-1088 | Deep Neural Solver for Math Word Problems | https://aclanthology.org/D17-1088/ | [
"Yan Wang",
"Xiaojiang Liu",
"Shuming Shi"
] | This paper presents a deep neural solver to automatically solve math word problems. In contrast to previous statistical learning approaches, we directly translate math word problems to equation templates using a recurrent neural network (RNN) model, without sophisticated feature engineering. We further design a hybrid ... | D17-1088 | 10.18653/v1/D17-1088 | null | null | null |
D17-1089 | Latent Space Embedding for Retrieval in Question-Answer Archives | https://aclanthology.org/D17-1089/ | [
"Deepak P",
"Dinesh Garg",
"Shirish Shevade"
] | Community-driven Question Answering (CQA) systems such as Yahoo! Answers have become valuable sources of reusable information. CQA retrieval enables usage of historical CQA archives to solve new questions posed by users. This task has received much recent attention, with methods building upon literature from translatio... | D17-1089 | 10.18653/v1/D17-1089 | null | null | null |
D17-1090 | Question Generation for Question Answering | https://aclanthology.org/D17-1090/ | [
"Nan Duan",
"Duyu Tang",
"Peng Chen",
"Ming Zhou"
] | This paper presents how to generate questions from given passages using neural networks, where large scale QA pairs are automatically crawled and processed from Community-QA website, and used as training data. The contribution of the paper is 2-fold: First, two types of question generation approaches are proposed, one ... | D17-1090 | 10.18653/v1/D17-1090 | null | null | null |
D17-1091 | Learning to Paraphrase for Question Answering | https://aclanthology.org/D17-1091/ | [
"Li Dong",
"Jonathan Mallinson",
"Siva Reddy",
"Mirella Lapata"
] | Question answering (QA) systems are sensitive to the many different ways natural language expresses the same information need. In this paper we turn to paraphrases as a means of capturing this knowledge and present a general framework which learns felicitous paraphrases for various QA tasks. Our method is trained end-t... | D17-1091 | 10.18653/v1/D17-1091 | null | 1708.06022 | title_snapshot |
D17-1092 | Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture | https://aclanthology.org/D17-1092/ | [
"Yuanliang Meng",
"Anna Rumshisky",
"Alexey Romanov"
] | In this paper, we propose to use a set of simple, uniform in architecture LSTM-based models to recover different kinds of temporal relations from text. Using the shortest dependency path between entities as input, the same architecture is used to extract intra-sentence, cross-sentence, and document creation time relati... | D17-1092 | 10.18653/v1/D17-1092 | null | 1703.05851 | title_snapshot |
D17-1093 | Ranking Kernels for Structures and Embeddings: A Hybrid Preference and Classification Model | https://aclanthology.org/D17-1093/ | [
"Kateryna Tymoshenko",
"Daniele Bonadiman",
"Alessandro Moschitti"
] | Recent work has shown that Tree Kernels (TKs) and Convolutional Neural Networks (CNNs) obtain the state of the art in answer sentence reranking. Additionally, their combination used in Support Vector Machines (SVMs) is promising as it can exploit both the syntactic patterns captured by TKs and the embeddings learned by... | D17-1093 | 10.18653/v1/D17-1093 | null | null | null |
D17-1094 | Recovering Question Answering Errors via Query Revision | https://aclanthology.org/D17-1094/ | [
"Semih Yavuz",
"Izzeddin Gur",
"Yu Su",
"Xifeng Yan"
] | The existing factoid QA systems often lack a post-inspection component that can help models recover from their own mistakes. In this work, we propose to crosscheck the corresponding KB relations behind the predicted answers and identify potential inconsistencies. Instead of developing a new model that accepts evidences... | D17-1094 | 10.18653/v1/D17-1094 | null | null | null |
D17-1095 | An empirical study on the effectiveness of images in Multimodal Neural Machine Translation | https://aclanthology.org/D17-1095/ | [
"Jean-Benoit Delbrouck",
"Stéphane Dupont"
] | In state-of-the-art Neural Machine Translation (NMT), an attention mechanism is used during decoding to enhance the translation. At every step, the decoder uses this mechanism to focus on different parts of the source sentence to gather the most useful information before outputting its target word. Recently, the effect... | D17-1095 | 10.18653/v1/D17-1095 | null | 1707.00995 | title_snapshot |
D17-1096 | Sound-Word2Vec: Learning Word Representations Grounded in Sounds | https://aclanthology.org/D17-1096/ | [
"Ashwin Vijayakumar",
"Ramakrishna Vedantam",
"Devi Parikh"
] | To be able to interact better with humans, it is crucial for machines to understand sound – a primary modality of human perception. Previous works have used sound to learn embeddings for improved generic semantic similarity assessment. In this work, we treat sound as a first-class citizen, studying downstream 6textual ... | D17-1096 | 10.18653/v1/D17-1096 | null | 1703.01720 | title_snapshot |
D17-1097 | The Promise of Premise: Harnessing Question Premises in Visual Question Answering | https://aclanthology.org/D17-1097/ | [
"Aroma Mahendru",
"Viraj Prabhu",
"Akrit Mohapatra",
"Dhruv Batra",
"Stefan Lee"
] | In this paper, we make a simple observation that questions about images often contain premises – objects and relationships implied by the question – and that reasoning about premises can help Visual Question Answering (VQA) models respond more intelligently to irrelevant or previously unseen questions. When presented w... | D17-1097 | 10.18653/v1/D17-1097 | null | 1705.00601 | title_snapshot |
D17-1098 | Guided Open Vocabulary Image Captioning with Constrained Beam Search | https://aclanthology.org/D17-1098/ | [
"Peter Anderson",
"Basura Fernando",
"Mark Johnson",
"Stephen Gould"
] | Existing image captioning models do not generalize well to out-of-domain images containing novel scenes or objects. This limitation severely hinders the use of these models in real world applications dealing with images in the wild. We address this problem using a flexible approach that enables existing deep captioning... | D17-1098 | 10.18653/v1/D17-1098 | null | 1612.00576 | title_snapshot |
D17-1099 | Zero-Shot Activity Recognition with Verb Attribute Induction | https://aclanthology.org/D17-1099/ | [
"Rowan Zellers",
"Yejin Choi"
] | In this paper, we investigate large-scale zero-shot activity recognition by modeling the visual and linguistic attributes of action verbs. For example, the verb “salute” has several properties, such as being a light movement, a social act, and short in duration. We use these attributes as the internal mapping between v... | D17-1099 | 10.18653/v1/D17-1099 | null | 1707.09468 | title_snapshot |
D17-1100 | Deriving continous grounded meaning representations from referentially structured multimodal contexts | https://aclanthology.org/D17-1100/ | [
"Sina Zarrieß",
"David Schlangen"
] | Corpora of referring expressions paired with their visual referents are a good source for learning word meanings directly grounded in visual representations. Here, we explore additional ways of extracting from them word representations linked to multi-modal context: through expressions that refer to the same object, an... | D17-1100 | 10.18653/v1/D17-1100 | null | null | null |
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