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 17 132 | paper_url stringlengths 34 34 | authors listlengths 1 12 | abstract large_stringlengths 201 1.61k | anthology_id stringlengths 8 8 | doi stringlengths 20 20 | award stringclasses 0
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D18-1001 | Privacy-preserving Neural Representations of Text | https://aclanthology.org/D18-1001/ | [
"Maximin Coavoux",
"Shashi Narayan",
"Shay B. Cohen"
] | This article deals with adversarial attacks towards deep learning systems for Natural Language Processing (NLP), in the context of privacy protection. We study a specific type of attack: an attacker eavesdrops on the hidden representations of a neural text classifier and tries to recover information about the input tex... | D18-1001 | 10.18653/v1/D18-1001 | null | 1808.09408 | title_snapshot |
D18-1002 | Adversarial Removal of Demographic Attributes from Text Data | https://aclanthology.org/D18-1002/ | [
"Yanai Elazar",
"Yoav Goldberg"
] | Recent advances in Representation Learning and Adversarial Training seem to succeed in removing unwanted features from the learned representation. We show that demographic information of authors is encoded in—and can be recovered from—the intermediate representations learned by text-based neural classifiers. The implic... | D18-1002 | 10.18653/v1/D18-1002 | null | 1808.06640 | title_snapshot |
D18-1003 | DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning | https://aclanthology.org/D18-1003/ | [
"Kashyap Popat",
"Subhabrata Mukherjee",
"Andrew Yates",
"Gerhard Weikum"
] | Misinformation such as fake news is one of the big challenges of our society. Research on automated fact-checking has proposed methods based on supervised learning, but these approaches do not consider external evidence apart from labeled training instances. Recent approaches counter this deficit by considering externa... | D18-1003 | 10.18653/v1/D18-1003 | null | 1809.06416 | title_snapshot |
D18-1004 | It’s going to be okay: Measuring Access to Support in Online Communities | https://aclanthology.org/D18-1004/ | [
"Zijian Wang",
"David Jurgens"
] | People use online platforms to seek out support for their informational and emotional needs. Here, we ask what effect does revealing one’s gender have on receiving support. To answer this, we create (i) a new dataset and method for identifying supportive replies and (ii) new methods for inferring gender from text and n... | D18-1004 | 10.18653/v1/D18-1004 | null | null | null |
D18-1005 | Detecting Gang-Involved Escalation on Social Media Using Context | https://aclanthology.org/D18-1005/ | [
"Serina Chang",
"Ruiqi Zhong",
"Ethan Adams",
"Fei-Tzin Lee",
"Siddharth Varia",
"Desmond Patton",
"William Frey",
"Chris Kedzie",
"Kathy McKeown"
] | Gang-involved youth in cities such as Chicago have increasingly turned to social media to post about their experiences and intents online. In some situations, when they experience the loss of a loved one, their online expression of emotion may evolve into aggression towards rival gangs and ultimately into real-world vi... | D18-1005 | 10.18653/v1/D18-1005 | null | 1809.03632 | title_snapshot |
D18-1006 | Reasoning about Actions and State Changes by Injecting Commonsense Knowledge | https://aclanthology.org/D18-1006/ | [
"Niket Tandon",
"Bhavana Dalvi",
"Joel Grus",
"Wen-tau Yih",
"Antoine Bosselut",
"Peter Clark"
] | Comprehending procedural text, e.g., a paragraph describing photosynthesis, requires modeling actions and the state changes they produce, so that questions about entities at different timepoints can be answered. Although several recent systems have shown impressive progress in this task, their predictions can be global... | D18-1006 | 10.18653/v1/D18-1006 | null | 1808.10012 | title_snapshot |
D18-1007 | Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation | https://aclanthology.org/D18-1007/ | [
"Adam Poliak",
"Aparajita Haldar",
"Rachel Rudinger",
"J. Edward Hu",
"Ellie Pavlick",
"Aaron Steven White",
"Benjamin Van Durme"
] | We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in ... | D18-1007 | 10.18653/v1/D18-1007 | null | 1804.08207 | title_snapshot |
D18-1008 | Textual Analogy Parsing: What’s Shared and What’s Compared among Analogous Facts | https://aclanthology.org/D18-1008/ | [
"Matthew Lamm",
"Arun Chaganty",
"Christopher D. Manning",
"Dan Jurafsky",
"Percy Liang"
] | To understand a sentence like “whereas only 10% of White Americans live at or below the poverty line, 28% of African Americans do” it is important not only to identify individual facts, e.g., poverty rates of distinct demographic groups, but also the higher-order relations between them, e.g., the disparity between them... | D18-1008 | 10.18653/v1/D18-1008 | null | 1809.02700 | title_snapshot |
D18-1009 | SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference | https://aclanthology.org/D18-1009/ | [
"Rowan Zellers",
"Yonatan Bisk",
"Roy Schwartz",
"Yejin Choi"
] | Given a partial description like “she opened the hood of the car,” humans can reason about the situation and anticipate what might come next (”then, she examined the engine”). In this paper, we introduce the task of grounded commonsense inference, unifying natural language inference and commonsense reasoning. We presen... | D18-1009 | 10.18653/v1/D18-1009 | null | 1808.05326 | title_snapshot |
D18-1010 | TwoWingOS: A Two-Wing Optimization Strategy for Evidential Claim Verification | https://aclanthology.org/D18-1010/ | [
"Wenpeng Yin",
"Dan Roth"
] | Determining whether a given claim is supported by evidence is a fundamental NLP problem that is best modeled as Textual Entailment. However, given a large collection of text, finding evidence that could support or refute a given claim is a challenge in itself, amplified by the fact that different evidence might be need... | D18-1010 | 10.18653/v1/D18-1010 | null | 1808.03465 | title_snapshot |
D18-1011 | Associative Multichannel Autoencoder for Multimodal Word Representation | https://aclanthology.org/D18-1011/ | [
"Shaonan Wang",
"Jiajun Zhang",
"Chengqing Zong"
] | In this paper we address the problem of learning multimodal word representations by integrating textual, visual and auditory inputs. Inspired by the re-constructive and associative nature of human memory, we propose a novel associative multichannel autoencoder (AMA). Our model first learns the associations between text... | D18-1011 | 10.18653/v1/D18-1011 | null | null | null |
D18-1012 | Game-Based Video-Context Dialogue | https://aclanthology.org/D18-1012/ | [
"Ramakanth Pasunuru",
"Mohit Bansal"
] | Current dialogue systems focus more on textual and speech context knowledge and are usually based on two speakers. Some recent work has investigated static image-based dialogue. However, several real-world human interactions also involve dynamic visual context (similar to videos) as well as dialogue exchanges among mul... | D18-1012 | 10.18653/v1/D18-1012 | null | 1809.04560 | title_snapshot |
D18-1013 | simNet: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions | https://aclanthology.org/D18-1013/ | [
"Fenglin Liu",
"Xuancheng Ren",
"Yuanxin Liu",
"Houfeng Wang",
"Xu Sun"
] | The encode-decoder framework has shown recent success in image captioning. Visual attention, which is good at detailedness, and semantic attention, which is good at comprehensiveness, have been separately proposed to ground the caption on the image. In this paper, we propose the Stepwise Image-Topic Merging Network (si... | D18-1013 | 10.18653/v1/D18-1013 | null | 1808.08732 | title_snapshot |
D18-1014 | Multimodal Language Analysis with Recurrent Multistage Fusion | https://aclanthology.org/D18-1014/ | [
"Paul Pu Liang",
"Ziyin Liu",
"AmirAli Bagher Zadeh",
"Louis-Philippe Morency"
] | Computational modeling of human multimodal language is an emerging research area in natural language processing spanning the language, visual and acoustic modalities. Comprehending multimodal language requires modeling not only the interactions within each modality (intra-modal interactions) but more importantly the in... | D18-1014 | 10.18653/v1/D18-1014 | null | 1808.03920 | title_snapshot |
D18-1015 | Temporally Grounding Natural Sentence in Video | https://aclanthology.org/D18-1015/ | [
"Jingyuan Chen",
"Xinpeng Chen",
"Lin Ma",
"Zequn Jie",
"Tat-Seng Chua"
] | We introduce an effective and efficient method that grounds (i.e., localizes) natural sentences in long, untrimmed video sequences. Specifically, a novel Temporal GroundNet (TGN) is proposed to temporally capture the evolving fine-grained frame-by-word interactions between video and sentence. TGN sequentially scores a ... | D18-1015 | 10.18653/v1/D18-1015 | null | null | null |
D18-1016 | PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution | https://aclanthology.org/D18-1016/ | [
"Hong Chen",
"Zhenhua Fan",
"Hao Lu",
"Alan Yuille",
"Shu Rong"
] | We introduce PreCo, a large-scale English dataset for coreference resolution. The dataset is designed to embody the core challenges in coreference, such as entity representation, by alleviating the challenge of low overlap between training and test sets and enabling separated analysis of mention detection and mention c... | D18-1016 | 10.18653/v1/D18-1016 | null | 1810.09807 | title_snapshot |
D18-1017 | Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism | https://aclanthology.org/D18-1017/ | [
"Pengfei Cao",
"Yubo Chen",
"Kang Liu",
"Jun Zhao",
"Shengping Liu"
] | Named entity recognition (NER) is an important task in natural language processing area, which needs to determine entities boundaries and classify them into pre-defined categories. For Chinese NER task, there is only a very small amount of annotated data available. Chinese NER task and Chinese word segmentation (CWS) t... | D18-1017 | 10.18653/v1/D18-1017 | null | null | null |
D18-1018 | Using Linguistic Features to Improve the Generalization Capability of Neural Coreference Resolvers | https://aclanthology.org/D18-1018/ | [
"Nafise Sadat Moosavi",
"Michael Strube"
] | Coreference resolution is an intermediate step for text understanding. It is used in tasks and domains for which we do not necessarily have coreference annotated corpora. Therefore, generalization is of special importance for coreference resolution. However, while recent coreference resolvers have notable improvements ... | D18-1018 | 10.18653/v1/D18-1018 | null | 1708.00160 | title_snapshot |
D18-1019 | Neural Segmental Hypergraphs for Overlapping Mention Recognition | https://aclanthology.org/D18-1019/ | [
"Bailin Wang",
"Wei Lu"
] | In this work, we propose a novel segmental hypergraph representation to model overlapping entity mentions that are prevalent in many practical datasets. We show that our model built on top of such a new representation is able to capture features and interactions that cannot be captured by previous models while maintain... | D18-1019 | 10.18653/v1/D18-1019 | null | 1810.01817 | title_snapshot |
D18-1020 | Variational Sequential Labelers for Semi-Supervised Learning | https://aclanthology.org/D18-1020/ | [
"Mingda Chen",
"Qingming Tang",
"Karen Livescu",
"Kevin Gimpel"
] | We introduce a family of multitask variational methods for semi-supervised sequence labeling. Our model family consists of a latent-variable generative model and a discriminative labeler. The generative models use latent variables to define the conditional probability of a word given its context, drawing inspiration fr... | D18-1020 | 10.18653/v1/D18-1020 | null | 1906.09535 | title_snapshot |
D18-1021 | Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision | https://aclanthology.org/D18-1021/ | [
"Yixin Cao",
"Lei Hou",
"Juanzi Li",
"Zhiyuan Liu",
"Chengjiang Li",
"Xu Chen",
"Tiansi Dong"
] | Jointly representation learning of words and entities benefits many NLP tasks, but has not been well explored in cross-lingual settings. In this paper, we propose a novel method for joint representation learning of cross-lingual words and entities. It captures mutually complementary knowledge, and enables cross-lingual... | D18-1021 | 10.18653/v1/D18-1021 | null | 1811.10776 | title_snapshot |
D18-1022 | Deep Pivot-Based Modeling for Cross-language Cross-domain Transfer with Minimal Guidance | https://aclanthology.org/D18-1022/ | [
"Yftah Ziser",
"Roi Reichart"
] | While cross-domain and cross-language transfer have long been prominent topics in NLP research, their combination has hardly been explored. In this work we consider this problem, and propose a framework that builds on pivot-based learning, structure-aware Deep Neural Networks (particularly LSTMs and CNNs) and bilingual... | D18-1022 | 10.18653/v1/D18-1022 | null | null | null |
D18-1023 | Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding | https://aclanthology.org/D18-1023/ | [
"Lifu Huang",
"Kyunghyun Cho",
"Boliang Zhang",
"Heng Ji",
"Kevin Knight"
] | We construct a multilingual common semantic space based on distributional semantics, where words from multiple languages are projected into a shared space via which all available resources and knowledge can be shared across multiple languages. Beyond word alignment, we introduce multiple cluster-level alignments and en... | D18-1023 | 10.18653/v1/D18-1023 | null | 1804.07875 | title_snapshot |
D18-1024 | Unsupervised Multilingual Word Embeddings | https://aclanthology.org/D18-1024/ | [
"Xilun Chen",
"Claire Cardie"
] | Multilingual Word Embeddings (MWEs) represent words from multiple languages in a single distributional vector space. Unsupervised MWE (UMWE) methods acquire multilingual embeddings without cross-lingual supervision, which is a significant advantage over traditional supervised approaches and opens many new possibilities... | D18-1024 | 10.18653/v1/D18-1024 | null | 1808.08933 | title_snapshot |
D18-1025 | CLUSE: Cross-Lingual Unsupervised Sense Embeddings | https://aclanthology.org/D18-1025/ | [
"Ta-Chung Chi",
"Yun-Nung Chen"
] | This paper proposes a modularized sense induction and representation learning model that jointly learns bilingual sense embeddings that align well in the vector space, where the cross-lingual signal in the English-Chinese parallel corpus is exploited to capture the collocation and distributed characteristics in the lan... | D18-1025 | 10.18653/v1/D18-1025 | null | 1809.05694 | title_snapshot |
D18-1026 | Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization | https://aclanthology.org/D18-1026/ | [
"Edoardo Maria Ponti",
"Ivan Vulić",
"Goran Glavaš",
"Nikola Mrkšić",
"Anna Korhonen"
] | Semantic specialization is a process of fine-tuning pre-trained distributional word vectors using external lexical knowledge (e.g., WordNet) to accentuate a particular semantic relation in the specialized vector space. While post-processing specialization methods are applicable to arbitrary distributional vectors, they... | D18-1026 | 10.18653/v1/D18-1026 | null | 1809.04163 | title_snapshot |
D18-1027 | Improving Cross-Lingual Word Embeddings by Meeting in the Middle | https://aclanthology.org/D18-1027/ | [
"Yerai Doval",
"Jose Camacho-Collados",
"Luis Espinosa-Anke",
"Steven Schockaert"
] | Cross-lingual word embeddings are becoming increasingly important in multilingual NLP. Recently, it has been shown that these embeddings can be effectively learned by aligning two disjoint monolingual vector spaces through linear transformations, using no more than a small bilingual dictionary as supervision. In this w... | D18-1027 | 10.18653/v1/D18-1027 | null | 1808.08780 | title_snapshot |
D18-1028 | WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse | https://aclanthology.org/D18-1028/ | [
"Manaal Faruqui",
"Ellie Pavlick",
"Ian Tenney",
"Dipanjan Das"
] | We release a corpus of 43 million atomic edits across 8 languages. These edits are mined from Wikipedia edit history and consist of instances in which a human editor has inserted a single contiguous phrase into, or deleted a single contiguous phrase from, an existing sentence. We use the collected data to show that the... | D18-1028 | 10.18653/v1/D18-1028 | null | 1808.09422 | title_snapshot |
D18-1029 | On the Relation between Linguistic Typology and (Limitations of) Multilingual Language Modeling | https://aclanthology.org/D18-1029/ | [
"Daniela Gerz",
"Ivan Vulić",
"Edoardo Maria Ponti",
"Roi Reichart",
"Anna Korhonen"
] | A key challenge in cross-lingual NLP is developing general language-independent architectures that are equally applicable to any language. However, this ambition is largely hampered by the variation in structural and semantic properties, i.e. the typological profiles of the world’s languages. In this work, we analyse t... | D18-1029 | 10.18653/v1/D18-1029 | null | null | null |
D18-1030 | A Fast, Compact, Accurate Model for Language Identification of Codemixed Text | https://aclanthology.org/D18-1030/ | [
"Yuan Zhang",
"Jason Riesa",
"Daniel Gillick",
"Anton Bakalov",
"Jason Baldridge",
"David Weiss"
] | We address fine-grained multilingual language identification: providing a language code for every token in a sentence, including codemixed text containing multiple languages. Such text is prevalent online, in documents, social media, and message boards. We show that a feed-forward network with a simple globally constra... | D18-1030 | 10.18653/v1/D18-1030 | null | 1810.04142 | title_snapshot |
D18-1031 | Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning | https://aclanthology.org/D18-1031/ | [
"Weichao Wang",
"Shi Feng",
"Wei Gao",
"Daling Wang",
"Yifei Zhang"
] | Sentiment expression in microblog posts can be affected by user’s personal character, opinion bias, political stance and so on. Most of existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. We observed that microblog users h... | D18-1031 | 10.18653/v1/D18-1031 | null | null | null |
D18-1032 | Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks | https://aclanthology.org/D18-1032/ | [
"Zhichun Wang",
"Qingsong Lv",
"Xiaohan Lan",
"Yu Zhang"
] | Multilingual knowledge graphs (KGs) such as DBpedia and YAGO contain structured knowledge of entities in several distinct languages, and they are useful resources for cross-lingual AI and NLP applications. Cross-lingual KG alignment is the task of matching entities with their counterparts in different languages, which ... | D18-1032 | 10.18653/v1/D18-1032 | null | null | null |
D18-1033 | Cross-lingual Lexical Sememe Prediction | https://aclanthology.org/D18-1033/ | [
"Fanchao Qi",
"Yankai Lin",
"Maosong Sun",
"Hao Zhu",
"Ruobing Xie",
"Zhiyuan Liu"
] | Sememes are defined as the minimum semantic units of human languages. As important knowledge sources, sememe-based linguistic knowledge bases have been widely used in many NLP tasks. However, most languages still do not have sememe-based linguistic knowledge bases. Thus we present a task of cross-lingual lexical sememe... | D18-1033 | 10.18653/v1/D18-1033 | null | null | null |
D18-1034 | Neural Cross-Lingual Named Entity Recognition with Minimal Resources | https://aclanthology.org/D18-1034/ | [
"Jiateng Xie",
"Zhilin Yang",
"Graham Neubig",
"Noah A. Smith",
"Jaime Carbonell"
] | For languages with no annotated resources, unsupervised transfer of natural language processing models such as named-entity recognition (NER) from resource-rich languages would be an appealing capability. However, differences in words and word order across languages make it a challenging problem. To improve mapping of ... | D18-1034 | 10.18653/v1/D18-1034 | null | 1808.09861 | title_snapshot |
D18-1035 | A Stable and Effective Learning Strategy for Trainable Greedy Decoding | https://aclanthology.org/D18-1035/ | [
"Yun Chen",
"Victor O.K. Li",
"Kyunghyun Cho",
"Samuel R. Bowman"
] | Beam search is a widely used approximate search strategy for neural network decoders, and it generally outperforms simple greedy decoding on tasks like machine translation. However, this improvement comes at substantial computational cost. In this paper, we propose a flexible new method that allows us to reap nearly th... | D18-1035 | 10.18653/v1/D18-1035 | null | 1804.07915 | title_snapshot |
D18-1036 | Addressing Troublesome Words in Neural Machine Translation | https://aclanthology.org/D18-1036/ | [
"Yang Zhao",
"Jiajun Zhang",
"Zhongjun He",
"Chengqing Zong",
"Hua Wu"
] | One of the weaknesses of Neural Machine Translation (NMT) is in handling lowfrequency and ambiguous words, which we refer as troublesome words. To address this problem, we propose a novel memoryenhanced NMT method. First, we investigate different strategies to define and detect the troublesome words. Then, a contextual... | D18-1036 | 10.18653/v1/D18-1036 | null | null | null |
D18-1037 | Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing | https://aclanthology.org/D18-1037/ | [
"Jetic Gū",
"Hassan S. Shavarani",
"Anoop Sarkar"
] | The addition of syntax-aware decoding in Neural Machine Translation (NMT) systems requires an effective tree-structured neural network, a syntax-aware attention model and a language generation model that is sensitive to sentence structure. Recent approaches resort to sequential decoding by adding additional neural netw... | D18-1037 | 10.18653/v1/D18-1037 | null | 1809.01854 | title_snapshot |
D18-1038 | XL-NBT: A Cross-lingual Neural Belief Tracking Framework | https://aclanthology.org/D18-1038/ | [
"Wenhu Chen",
"Jianshu Chen",
"Yu Su",
"Xin Wang",
"Dong Yu",
"Xifeng Yan",
"William Yang Wang"
] | Task-oriented dialog systems are becoming pervasive, and many companies heavily rely on them to complement human agents for customer service in call centers. With globalization, the need for providing cross-lingual customer support becomes more urgent than ever. However, cross-lingual support poses great challenges—it ... | D18-1038 | 10.18653/v1/D18-1038 | null | 1808.06244 | title_snapshot |
D18-1039 | Contextual Parameter Generation for Universal Neural Machine Translation | https://aclanthology.org/D18-1039/ | [
"Emmanouil Antonios Platanios",
"Mrinmaya Sachan",
"Graham Neubig",
"Tom Mitchell"
] | We propose a simple modification to existing neural machine translation (NMT) models that enables using a single universal model to translate between multiple languages while allowing for language specific parameterization, and that can also be used for domain adaptation. Our approach requires no changes to the model a... | D18-1039 | 10.18653/v1/D18-1039 | null | 1808.08493 | title_snapshot |
D18-1040 | Back-Translation Sampling by Targeting Difficult Words in Neural Machine Translation | https://aclanthology.org/D18-1040/ | [
"Marzieh Fadaee",
"Christof Monz"
] | Neural Machine Translation has achieved state-of-the-art performance for several language pairs using a combination of parallel and synthetic data. Synthetic data is often generated by back-translating sentences randomly sampled from monolingual data using a reverse translation model. While back-translation has been sh... | D18-1040 | 10.18653/v1/D18-1040 | null | 1808.09006 | title_snapshot |
D18-1041 | Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination | https://aclanthology.org/D18-1041/ | [
"Jiali Zeng",
"Jinsong Su",
"Huating Wen",
"Yang Liu",
"Jun Xie",
"Yongjing Yin",
"Jianqiang Zhao"
] | With great practical value, the study of Multi-domain Neural Machine Translation (NMT) mainly focuses on using mixed-domain parallel sentences to construct a unified model that allows translation to switch between different domains. Intuitively, words in a sentence are related to its domain to varying degrees, so that ... | D18-1041 | 10.18653/v1/D18-1041 | null | null | null |
D18-1042 | A Discriminative Latent-Variable Model for Bilingual Lexicon Induction | https://aclanthology.org/D18-1042/ | [
"Sebastian Ruder",
"Ryan Cotterell",
"Yova Kementchedjhieva",
"Anders Søgaard"
] | We introduce a novel discriminative latent-variable model for the task of bilingual lexicon induction. Our model combines the bipartite matching dictionary prior of Haghighi et al. (2008) with a state-of-the-art embedding-based approach. To train the model, we derive an efficient Viterbi EM algorithm. We provide empiri... | D18-1042 | 10.18653/v1/D18-1042 | null | 1808.09334 | title_snapshot |
D18-1043 | Non-Adversarial Unsupervised Word Translation | https://aclanthology.org/D18-1043/ | [
"Yedid Hoshen",
"Lior Wolf"
] | Unsupervised word translation from non-parallel inter-lingual corpora has attracted much research interest. Very recently, neural network methods trained with adversarial loss functions achieved high accuracy on this task. Despite the impressive success of the recent techniques, they suffer from the typical drawbacks o... | D18-1043 | 10.18653/v1/D18-1043 | null | 1801.06126 | title_snapshot |
D18-1044 | Semi-Autoregressive Neural Machine Translation | https://aclanthology.org/D18-1044/ | [
"Chunqi Wang",
"Ji Zhang",
"Haiqing Chen"
] | Existing approaches to neural machine translation are typically autoregressive models. While these models attain state-of-the-art translation quality, they are suffering from low parallelizability and thus slow at decoding long sequences. In this paper, we propose a novel model for fast sequence generation — the semi-a... | D18-1044 | 10.18653/v1/D18-1044 | null | 1808.08583 | title_snapshot |
D18-1045 | Understanding Back-Translation at Scale | https://aclanthology.org/D18-1045/ | [
"Sergey Edunov",
"Myle Ott",
"Michael Auli",
"David Grangier"
] | An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences. This work broadens the understanding of back-translation and investigates a number of methods to generate synthetic source sentences. We find th... | D18-1045 | 10.18653/v1/D18-1045 | null | 1808.09381 | title_snapshot |
D18-1046 | Bootstrapping Transliteration with Constrained Discovery for Low-Resource Languages | https://aclanthology.org/D18-1046/ | [
"Shyam Upadhyay",
"Jordan Kodner",
"Dan Roth"
] | Generating the English transliteration of a name written in a foreign script is an important and challenging step in multilingual knowledge acquisition and information extraction. Existing approaches to transliteration generation require a large (>5000) number of training examples. This difficulty contrasts with transl... | D18-1046 | 10.18653/v1/D18-1046 | null | 1809.07807 | title_snapshot |
D18-1047 | NORMA: Neighborhood Sensitive Maps for Multilingual Word Embeddings | https://aclanthology.org/D18-1047/ | [
"Ndapa Nakashole"
] | Inducing multilingual word embeddings by learning a linear map between embedding spaces of different languages achieves remarkable accuracy on related languages. However, accuracy drops substantially when translating between distant languages. Given that languages exhibit differences in vocabulary, grammar, written for... | D18-1047 | 10.18653/v1/D18-1047 | null | null | null |
D18-1048 | Adaptive Multi-pass Decoder for Neural Machine Translation | https://aclanthology.org/D18-1048/ | [
"Xinwei Geng",
"Xiaocheng Feng",
"Bing Qin",
"Ting Liu"
] | Although end-to-end neural machine translation (NMT) has achieved remarkable progress in the recent years, the idea of adopting multi-pass decoding mechanism into conventional NMT is not well explored. In this paper, we propose a novel architecture called adaptive multi-pass decoder, which introduces a flexible multi-p... | D18-1048 | 10.18653/v1/D18-1048 | null | null | null |
D18-1049 | Improving the Transformer Translation Model with Document-Level Context | https://aclanthology.org/D18-1049/ | [
"Jiacheng Zhang",
"Huanbo Luan",
"Maosong Sun",
"Feifei Zhai",
"Jingfang Xu",
"Min Zhang",
"Yang Liu"
] | Although the Transformer translation model (Vaswani et al., 2017) has achieved state-of-the-art performance in a variety of translation tasks, how to use document-level context to deal with discourse phenomena problematic for Transformer still remains a challenge. In this work, we extend the Transformer model with a ne... | D18-1049 | 10.18653/v1/D18-1049 | null | 1810.03581 | title_snapshot |
D18-1050 | MTNT: A Testbed for Machine Translation of Noisy Text | https://aclanthology.org/D18-1050/ | [
"Paul Michel",
"Graham Neubig"
] | Noisy or non-standard input text can cause disastrous mistranslations in most modern Machine Translation (MT) systems, and there has been growing research interest in creating noise-robust MT systems. However, as of yet there are no publicly available parallel corpora of with naturally occurring noisy inputs and transl... | D18-1050 | 10.18653/v1/D18-1050 | null | 1809.00388 | title_snapshot |
D18-1051 | SimpleQuestions Nearly Solved: A New Upperbound and Baseline Approach | https://aclanthology.org/D18-1051/ | [
"Michael Petrochuk",
"Luke Zettlemoyer"
] | The SimpleQuestions dataset is one of the most commonly used benchmarks for studying single-relation factoid questions. In this paper, we present new evidence that this benchmark can be nearly solved by standard methods. First, we show that ambiguity in the data bounds performance at 83.4%; many questions have more tha... | D18-1051 | 10.18653/v1/D18-1051 | null | 1804.08798 | title_snapshot |
D18-1052 | Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension | https://aclanthology.org/D18-1052/ | [
"Minjoon Seo",
"Tom Kwiatkowski",
"Ankur Parikh",
"Ali Farhadi",
"Hannaneh Hajishirzi"
] | We formalize a new modular variant of current question answering tasks by enforcing complete independence of the document encoder from the question encoder. This formulation addresses a key challenge in machine comprehension by building a standalone representation of the document discourse. It additionally leads to a s... | D18-1052 | 10.18653/v1/D18-1052 | null | 1804.07726 | title_snapshot |
D18-1053 | Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering | https://aclanthology.org/D18-1053/ | [
"Jinhyuk Lee",
"Seongjun Yun",
"Hyunjae Kim",
"Miyoung Ko",
"Jaewoo Kang"
] | Recently, open-domain question answering (QA) has been combined with machine comprehension models to find answers in a large knowledge source. As open-domain QA requires retrieving relevant documents from text corpora to answer questions, its performance largely depends on the performance of document retrievers. Howeve... | D18-1053 | 10.18653/v1/D18-1053 | null | 1810.00494 | title_snapshot |
D18-1054 | Cut to the Chase: A Context Zoom-in Network for Reading Comprehension | https://aclanthology.org/D18-1054/ | [
"Sathish Reddy Indurthi",
"Seunghak Yu",
"Seohyun Back",
"Heriberto Cuayáhuitl"
] | In recent years many deep neural networks have been proposed to solve Reading Comprehension (RC) tasks. Most of these models suffer from reasoning over long documents and do not trivially generalize to cases where the answer is not present as a span in a given document. We present a novel neural-based architecture that... | D18-1054 | 10.18653/v1/D18-1054 | null | null | null |
D18-1055 | Adaptive Document Retrieval for Deep Question Answering | https://aclanthology.org/D18-1055/ | [
"Bernhard Kratzwald",
"Stefan Feuerriegel"
] | State-of-the-art systems in deep question answering proceed as follows: (1)an initial document retrieval selects relevant documents, which (2) are then processed by a neural network in order to extract the final answer. Yet the exact interplay between both components is poorly understood, especially concerning the numb... | D18-1055 | 10.18653/v1/D18-1055 | null | 1808.06528 | title_snapshot |
D18-1056 | Why is unsupervised alignment of English embeddings from different algorithms so hard? | https://aclanthology.org/D18-1056/ | [
"Mareike Hartmann",
"Yova Kementchedjhieva",
"Anders Søgaard"
] | This paper presents a challenge to the community: Generative adversarial networks (GANs) can perfectly align independent English word embeddings induced using the same algorithm, based on distributional information alone; but fails to do so, for two different embeddings algorithms. Why is that? We believe understanding... | D18-1056 | 10.18653/v1/D18-1056 | null | 1809.00150 | title_snapshot |
D18-1057 | Quantifying Context Overlap for Training Word Embeddings | https://aclanthology.org/D18-1057/ | [
"Yimeng Zhuang",
"Jinghui Xie",
"Yinhe Zheng",
"Xuan Zhu"
] | Most models for learning word embeddings are trained based on the context information of words, more precisely first order co-occurrence relations. In this paper, a metric is designed to estimate second order co-occurrence relations based on context overlap. The estimated values are further used as the augmented data t... | D18-1057 | 10.18653/v1/D18-1057 | null | null | null |
D18-1058 | Neural Latent Relational Analysis to Capture Lexical Semantic Relations in a Vector Space | https://aclanthology.org/D18-1058/ | [
"Koki Washio",
"Tsuneaki Kato"
] | Capturing the semantic relations of words in a vector space contributes to many natural language processing tasks. One promising approach exploits lexico-syntactic patterns as features of word pairs. In this paper, we propose a novel model of this pattern-based approach, neural latent relational analysis (NLRA). NLRA c... | D18-1058 | 10.18653/v1/D18-1058 | null | 1809.03401 | title_snapshot |
D18-1059 | Generalizing Word Embeddings using Bag of Subwords | https://aclanthology.org/D18-1059/ | [
"Jinman Zhao",
"Sidharth Mudgal",
"Yingyu Liang"
] | We approach the problem of generalizing pre-trained word embeddings beyond fixed-size vocabularies without using additional contextual information. We propose a subword-level word vector generation model that views words as bags of character n-grams. The model is simple, fast to train and provides good vectors for rare... | D18-1059 | 10.18653/v1/D18-1059 | null | 1809.04259 | title_snapshot |
D18-1060 | Neural Metaphor Detection in Context | https://aclanthology.org/D18-1060/ | [
"Ge Gao",
"Eunsol Choi",
"Yejin Choi",
"Luke Zettlemoyer"
] | We present end-to-end neural models for detecting metaphorical word use in context. We show that relatively standard BiLSTM models which operate on complete sentences work well in this setting, in comparison to previous work that used more restricted forms of linguistic context. These models establish a new state-of-th... | D18-1060 | 10.18653/v1/D18-1060 | null | 1808.09653 | title_snapshot |
D18-1061 | Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging | https://aclanthology.org/D18-1061/ | [
"Barbara Plank",
"Željko Agić"
] | a cross-lingual neural part-of-speech tagger that learns from disparate sources of distant supervision, and realistically scales to hundreds of low-resource languages. The model exploits annotation projection, instance selection, tag dictionaries, morphological lexicons, and distributed representations, all in a unifor... | D18-1061 | 10.18653/v1/D18-1061 | null | 1808.09733 | title_snapshot |
D18-1062 | Unsupervised Bilingual Lexicon Induction via Latent Variable Models | https://aclanthology.org/D18-1062/ | [
"Zi-Yi Dou",
"Zhi-Hao Zhou",
"Shujian Huang"
] | Bilingual lexicon extraction has been studied for decades and most previous methods have relied on parallel corpora or bilingual dictionaries. Recent studies have shown that it is possible to build a bilingual dictionary by aligning monolingual word embedding spaces in an unsupervised way. With the recent advances in g... | D18-1062 | 10.18653/v1/D18-1062 | null | null | null |
D18-1063 | Learning Unsupervised Word Translations Without Adversaries | https://aclanthology.org/D18-1063/ | [
"Tanmoy Mukherjee",
"Makoto Yamada",
"Timothy Hospedales"
] | Word translation, or bilingual dictionary induction, is an important capability that impacts many multilingual language processing tasks. Recent research has shown that word translation can be achieved in an unsupervised manner, without parallel seed dictionaries or aligned corpora. However, state of the art methods un... | D18-1063 | 10.18653/v1/D18-1063 | null | null | null |
D18-1064 | Adversarial Training for Multi-task and Multi-lingual Joint Modeling of Utterance Intent Classification | https://aclanthology.org/D18-1064/ | [
"Ryo Masumura",
"Yusuke Shinohara",
"Ryuichiro Higashinaka",
"Yushi Aono"
] | This paper proposes an adversarial training method for the multi-task and multi-lingual joint modeling needed for utterance intent classification. In joint modeling, common knowledge can be efficiently utilized among multiple tasks or multiple languages. This is achieved by introducing both language-specific networks s... | D18-1064 | 10.18653/v1/D18-1064 | null | null | null |
D18-1065 | Surprisingly Easy Hard-Attention for Sequence to Sequence Learning | https://aclanthology.org/D18-1065/ | [
"Shiv Shankar",
"Siddhant Garg",
"Sunita Sarawagi"
] | In this paper we show that a simple beam approximation of the joint distribution between attention and output is an easy, accurate, and efficient attention mechanism for sequence to sequence learning. The method combines the advantage of sharp focus in hard attention and the implementation ease of soft attention. On fi... | D18-1065 | 10.18653/v1/D18-1065 | null | null | null |
D18-1066 | Joint Learning for Emotion Classification and Emotion Cause Detection | https://aclanthology.org/D18-1066/ | [
"Ying Chen",
"Wenjun Hou",
"Xiyao Cheng",
"Shoushan Li"
] | We present a neural network-based joint approach for emotion classification and emotion cause detection, which attempts to capture mutual benefits across the two sub-tasks of emotion analysis. Considering that emotion classification and emotion cause detection need different kinds of features (affective and event-based... | D18-1066 | 10.18653/v1/D18-1066 | null | null | null |
D18-1067 | Exploring Optimism and Pessimism in Twitter Using Deep Learning | https://aclanthology.org/D18-1067/ | [
"Cornelia Caragea",
"Liviu P. Dinu",
"Bogdan Dumitru"
] | Identifying optimistic and pessimistic viewpoints and users from Twitter is useful for providing better social support to those who need such support, and for minimizing the negative influence among users and maximizing the spread of positive attitudes and ideas. In this paper, we explore a range of deep learning model... | D18-1067 | 10.18653/v1/D18-1067 | null | null | null |
D18-1068 | Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning | https://aclanthology.org/D18-1068/ | [
"Sotiris Lamprinidis",
"Daniel Hardt",
"Dirk Hovy"
] | Newspapers need to attract readers with headlines, anticipating their readers’ preferences. These preferences rely on topical, structural, and lexical factors. We model each of these factors in a multi-task GRU network to predict headline popularity. We find that pre-trained word embeddings provide significant improvem... | D18-1068 | 10.18653/v1/D18-1068 | null | null | null |
D18-1069 | Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates | https://aclanthology.org/D18-1069/ | [
"Di Chen",
"Jiachen Du",
"Lidong Bing",
"Ruifeng Xu"
] | Inferring the agreement/disagreement relation in debates, especially in online debates, is one of the fundamental tasks in argumentation mining. The expressions of agreement/disagreement usually rely on argumentative expressions in text as well as interactions between participants in debates. Previous works usually lac... | D18-1069 | 10.18653/v1/D18-1069 | null | null | null |
D18-1070 | Increasing In-Class Similarity by Retrofitting Embeddings with Demographic Information | https://aclanthology.org/D18-1070/ | [
"Dirk Hovy",
"Tommaso Fornaciari"
] | Most text-classification approaches represent the input based on textual features, either feature-based or continuous. However, this ignores strong non-linguistic similarities like homophily: people within a demographic group use language more similar to each other than to non-group members. We use homophily cues to re... | D18-1070 | 10.18653/v1/D18-1070 | null | null | null |
D18-1071 | A Syntactically Constrained Bidirectional-Asynchronous Approach for Emotional Conversation Generation | https://aclanthology.org/D18-1071/ | [
"Jingyuan Li",
"Xiao Sun"
] | Traditional neural language models tend to generate generic replies with poor logic and no emotion. In this paper, a syntactically constrained bidirectional-asynchronous approach for emotional conversation generation (E-SCBA) is proposed to address this issue. In our model, pre-generated emotion keywords and topic keyw... | D18-1071 | 10.18653/v1/D18-1071 | null | 1806.07000 | title_snapshot |
D18-1072 | Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning | https://aclanthology.org/D18-1072/ | [
"Chen Shi",
"Qi Chen",
"Lei Sha",
"Sujian Li",
"Xu Sun",
"Houfeng Wang",
"Lintao Zhang"
] | The lack of labeled data is one of the main challenges when building a task-oriented dialogue system. Existing dialogue datasets usually rely on human labeling, which is expensive, limited in size, and in low coverage. In this paper, we instead propose our framework auto-dialabel to automatically cluster the dialogue i... | D18-1072 | 10.18653/v1/D18-1072 | null | null | null |
D18-1073 | Extending Neural Generative Conversational Model using External Knowledge Sources | https://aclanthology.org/D18-1073/ | [
"Prasanna Parthasarathi",
"Joelle Pineau"
] | The use of connectionist approaches in conversational agents has been progressing rapidly due to the availability of large corpora. However current generative dialogue models often lack coherence and are content poor. This work proposes an architecture to incorporate unstructured knowledge sources to enhance the next u... | D18-1073 | 10.18653/v1/D18-1073 | null | 1809.05524 | title_snapshot |
D18-1074 | Modeling Temporality of Human Intentions by Domain Adaptation | https://aclanthology.org/D18-1074/ | [
"Xiaolei Huang",
"Lixing Liu",
"Kate Carey",
"Joshua Woolley",
"Stefan Scherer",
"Brian Borsari"
] | Categorizing patient’s intentions in conversational assessment can help decision making in clinical treatments. Many conversation corpora span broaden a series of time stages. However, it is not clear that how the themes shift in the conversation impact on the performance of human intention categorization (eg., patient... | D18-1074 | 10.18653/v1/D18-1074 | null | null | null |
D18-1075 | An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation | https://aclanthology.org/D18-1075/ | [
"Liangchen Luo",
"Jingjing Xu",
"Junyang Lin",
"Qi Zeng",
"Xu Sun"
] | Generating semantically coherent responses is still a major challenge in dialogue generation. Different from conventional text generation tasks, the mapping between inputs and responses in conversations is more complicated, which highly demands the understanding of utterance-level semantic dependency, a relation betwee... | D18-1075 | 10.18653/v1/D18-1075 | null | 1808.08795 | title_snapshot |
D18-1076 | A Dataset for Document Grounded Conversations | https://aclanthology.org/D18-1076/ | [
"Kangyan Zhou",
"Shrimai Prabhumoye",
"Alan W Black"
] | This paper introduces a document grounded dataset for conversations. We define “Document Grounded Conversations” as conversations that are about the contents of a specified document. In this dataset the specified documents were Wikipedia articles about popular movies. The dataset contains 4112 conversations with an ave... | D18-1076 | 10.18653/v1/D18-1076 | null | 1809.07358 | title_snapshot |
D18-1077 | Out-of-domain Detection based on Generative Adversarial Network | https://aclanthology.org/D18-1077/ | [
"Seonghan Ryu",
"Sangjun Koo",
"Hwanjo Yu",
"Gary Geunbae Lee"
] | The main goal of this paper is to develop out-of-domain (OOD) detection for dialog systems. We propose to use only in-domain (IND) sentences to build a generative adversarial network (GAN) of which the discriminator generates low scores for OOD sentences. To improve basic GANs, we apply feature matching loss in the dis... | D18-1077 | 10.18653/v1/D18-1077 | null | null | null |
D18-1078 | Listening Comprehension over Argumentative Content | https://aclanthology.org/D18-1078/ | [
"Shachar Mirkin",
"Guy Moshkowich",
"Matan Orbach",
"Lili Kotlerman",
"Yoav Kantor",
"Tamar Lavee",
"Michal Jacovi",
"Yonatan Bilu",
"Ranit Aharonov",
"Noam Slonim"
] | This paper presents a task for machine listening comprehension in the argumentation domain and a corresponding dataset in English. We recorded 200 spontaneous speeches arguing for or against 50 controversial topics. For each speech, we formulated a question, aimed at confirming or rejecting the occurrence of potential ... | D18-1078 | 10.18653/v1/D18-1078 | null | null | null |
D18-1079 | Using active learning to expand training data for implicit discourse relation recognition | https://aclanthology.org/D18-1079/ | [
"Yang Xu",
"Yu Hong",
"Huibin Ruan",
"Jianmin Yao",
"Min Zhang",
"Guodong Zhou"
] | We tackle discourse-level relation recognition, a problem of determining semantic relations between text spans. Implicit relation recognition is challenging due to the lack of explicit relational clues. The increasingly popular neural network techniques have been proven effective for semantic encoding, whereby widely e... | D18-1079 | 10.18653/v1/D18-1079 | null | null | null |
D18-1080 | Learning To Split and Rephrase From Wikipedia Edit History | https://aclanthology.org/D18-1080/ | [
"Jan A. Botha",
"Manaal Faruqui",
"John Alex",
"Jason Baldridge",
"Dipanjan Das"
] | Split and rephrase is the task of breaking down a sentence into shorter ones that together convey the same meaning. We extract a rich new dataset for this task by mining Wikipedia’s edit history: WikiSplit contains one million naturally occurring sentence rewrites, providing sixty times more distinct split examples and... | D18-1080 | 10.18653/v1/D18-1080 | null | 1808.09468 | title_snapshot |
D18-1081 | BLEU is Not Suitable for the Evaluation of Text Simplification | https://aclanthology.org/D18-1081/ | [
"Elior Sulem",
"Omri Abend",
"Ari Rappoport"
] | BLEU is widely considered to be an informative metric for text-to-text generation, including Text Simplification (TS). TS includes both lexical and structural aspects. In this paper we show that BLEU is not suitable for the evaluation of sentence splitting, the major structural simplification operation. We manually com... | D18-1081 | 10.18653/v1/D18-1081 | null | 1810.05995 | title_snapshot |
D18-1082 | S2SPMN: A Simple and Effective Framework for Response Generation with Relevant Information | https://aclanthology.org/D18-1082/ | [
"Jiaxin Pei",
"Chenliang Li"
] | How to generate relevant and informative responses is one of the core topics in response generation area. Following the task formulation of machine translation, previous works mainly consider response generation task as a mapping from a source sentence to a target sentence. To realize this mapping, existing works tend ... | D18-1082 | 10.18653/v1/D18-1082 | null | null | null |
D18-1083 | Improving Reinforcement Learning Based Image Captioning with Natural Language Prior | https://aclanthology.org/D18-1083/ | [
"Tszhang Guo",
"Shiyu Chang",
"Mo Yu",
"Kun Bai"
] | Recently, Reinforcement Learning (RL) approaches have demonstrated advanced performance in image captioning by directly optimizing the metric used for testing. However, this shaped reward introduces learning biases, which reduces the readability of generated text. In addition, the large sample space makes training unst... | D18-1083 | 10.18653/v1/D18-1083 | null | 1809.06227 | title_snapshot |
D18-1084 | Training for Diversity in Image Paragraph Captioning | https://aclanthology.org/D18-1084/ | [
"Luke Melas-Kyriazi",
"Alexander Rush",
"George Han"
] | Image paragraph captioning models aim to produce detailed descriptions of a source image. These models use similar techniques as standard image captioning models, but they have encountered issues in text generation, notably a lack of diversity between sentences, that have limited their effectiveness. In this work, we c... | D18-1084 | 10.18653/v1/D18-1084 | null | null | null |
D18-1085 | A Graph-theoretic Summary Evaluation for ROUGE | https://aclanthology.org/D18-1085/ | [
"Elaheh ShafieiBavani",
"Mohammad Ebrahimi",
"Raymond Wong",
"Fang Chen"
] | ROUGE is one of the first and most widely used evaluation metrics for text summarization. However, its assessment merely relies on surface similarities between peer and model summaries. Consequently, ROUGE is unable to fairly evaluate summaries including lexical variations and paraphrasing. We propose a graph-based app... | D18-1085 | 10.18653/v1/D18-1085 | null | null | null |
D18-1086 | Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation | https://aclanthology.org/D18-1086/ | [
"Hardy",
"Andreas Vlachos"
] | Recent work on abstractive summarization has made progress with neural encoder-decoder architectures. However, such models are often challenged due to their lack of explicit semantic modeling of the source document and its summary. In this paper, we extend previous work on abstractive summarization using Abstract Meani... | D18-1086 | 10.18653/v1/D18-1086 | null | 1808.09160 | title_snapshot |
D18-1087 | Evaluating Multiple System Summary Lengths: A Case Study | https://aclanthology.org/D18-1087/ | [
"Ori Shapira",
"David Gabay",
"Hadar Ronen",
"Judit Bar-Ilan",
"Yael Amsterdamer",
"Ani Nenkova",
"Ido Dagan"
] | Practical summarization systems are expected to produce summaries of varying lengths, per user needs. While a couple of early summarization benchmarks tested systems across multiple summary lengths, this practice was mostly abandoned due to the assumed cost of producing reference summaries of multiple lengths. In this ... | D18-1087 | 10.18653/v1/D18-1087 | null | null | null |
D18-1088 | Neural Latent Extractive Document Summarization | https://aclanthology.org/D18-1088/ | [
"Xingxing Zhang",
"Mirella Lapata",
"Furu Wei",
"Ming Zhou"
] | Extractive summarization models need sentence level labels, which are usually created with rule-based methods since most summarization datasets only have document summary pairs. These labels might be suboptimal. We propose a latent variable extractive model, where sentences are viewed as latent variables and sentences ... | D18-1088 | 10.18653/v1/D18-1088 | null | 1808.07187 | title_snapshot |
D18-1089 | On the Abstractiveness of Neural Document Summarization | https://aclanthology.org/D18-1089/ | [
"Fangfang Zhang",
"Jin-ge Yao",
"Rui Yan"
] | Many modern neural document summarization systems based on encoder-decoder networks are designed to produce abstractive summaries. We attempted to verify the degree of abstractiveness of modern neural abstractive summarization systems by calculating overlaps in terms of various types of units. Upon the observation that... | D18-1089 | 10.18653/v1/D18-1089 | null | null | null |
D18-1090 | Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning | https://aclanthology.org/D18-1090/ | [
"Yucheng Wang",
"Zhongyu Wei",
"Yaqian Zhou",
"Xuanjing Huang"
] | Automatic essay scoring (AES) is the task of assigning grades to essays without human interference. Existing systems for AES are typically trained to predict the score of each single essay at a time without considering the rating schema. In order to address this issue, we propose a reinforcement learning framework for ... | D18-1090 | 10.18653/v1/D18-1090 | null | null | null |
D18-1091 | Identifying Well-formed Natural Language Questions | https://aclanthology.org/D18-1091/ | [
"Manaal Faruqui",
"Dipanjan Das"
] | Understanding search queries is a hard problem as it involves dealing with “word salad” text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, ide... | D18-1091 | 10.18653/v1/D18-1091 | null | 1808.09419 | title_snapshot |
D18-1092 | Self-Governing Neural Networks for On-Device Short Text Classification | https://aclanthology.org/D18-1092/ | [
"Sujith Ravi",
"Zornitsa Kozareva"
] | Deep neural networks reach state-of-the-art performance for wide range of natural language processing, computer vision and speech applications. Yet, one of the biggest challenges is running these complex networks on devices such as mobile phones or smart watches with tiny memory footprint and low computational capacity... | D18-1092 | 10.18653/v1/D18-1092 | null | null | null |
D18-1093 | HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text Categorization | https://aclanthology.org/D18-1093/ | [
"Kazuya Shimura",
"Jiyi Li",
"Fumiyo Fukumoto"
] | We focus on the multi-label categorization task for short texts and explore the use of a hierarchical structure (HS) of categories. In contrast to the existing work using non-hierarchical flat model, the method leverages the hierarchical relations between the pre-defined categories to tackle the data sparsity problem. ... | D18-1093 | 10.18653/v1/D18-1093 | null | null | null |
D18-1094 | A Hierarchical Neural Attention-based Text Classifier | https://aclanthology.org/D18-1094/ | [
"Koustuv Sinha",
"Yue Dong",
"Jackie Chi Kit Cheung",
"Derek Ruths"
] | Deep neural networks have been displaying superior performance over traditional supervised classifiers in text classification. They learn to extract useful features automatically when sufficient amount of data is presented. However, along with the growth in the number of documents comes the increase in the number of ca... | D18-1094 | 10.18653/v1/D18-1094 | null | null | null |
D18-1095 | Labeled Anchors and a Scalable, Transparent, and Interactive Classifier | https://aclanthology.org/D18-1095/ | [
"Jeffrey Lund",
"Stephen Cowley",
"Wilson Fearn",
"Emily Hales",
"Kevin Seppi"
] | We propose Labeled Anchors, an interactive and supervised topic model based on the anchor words algorithm (Arora et al., 2013). Labeled Anchors is similar to Supervised Anchors (Nguyen et al., 2014) in that it extends the vector-space representation of words to include document labels. However, our formulation also adm... | D18-1095 | 10.18653/v1/D18-1095 | null | null | null |
D18-1096 | Coherence-Aware Neural Topic Modeling | https://aclanthology.org/D18-1096/ | [
"Ran Ding",
"Ramesh Nallapati",
"Bing Xiang"
] | Topic models are evaluated based on their ability to describe documents well (i.e. low perplexity) and to produce topics that carry coherent semantic meaning. In topic modeling so far, perplexity is a direct optimization target. However, topic coherence, owing to its challenging computation, is not optimized for and is... | D18-1096 | 10.18653/v1/D18-1096 | null | 1809.02687 | title_snapshot |
D18-1097 | Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models | https://aclanthology.org/D18-1097/ | [
"Daniel Watson",
"Nasser Zalmout",
"Nizar Habash"
] | Text normalization is an important enabling technology for several NLP tasks. Recently, neural-network-based approaches have outperformed well-established models in this task. However, in languages other than English, there has been little exploration in this direction. Both the scarcity of annotated data and the compl... | D18-1097 | 10.18653/v1/D18-1097 | null | 1809.01534 | title_snapshot |
D18-1098 | Topic Intrusion for Automatic Topic Model Evaluation | https://aclanthology.org/D18-1098/ | [
"Shraey Bhatia",
"Jey Han Lau",
"Timothy Baldwin"
] | Topic coherence is increasingly being used to evaluate topic models and filter topics for end-user applications. Topic coherence measures how well topic words relate to each other, but offers little insight on the utility of the topics in describing the documents. In this paper, we explore the topic intrusion task — th... | D18-1098 | 10.18653/v1/D18-1098 | null | null | null |
D18-1099 | Supervised and Unsupervised Methods for Robust Separation of Section Titles and Prose Text in Web Documents | https://aclanthology.org/D18-1099/ | [
"Abhijith Athreya Mysore Gopinath",
"Shomir Wilson",
"Norman Sadeh"
] | The text in many web documents is organized into a hierarchy of section titles and corresponding prose content, a structure which provides potentially exploitable information on discourse structure and topicality. However, this organization is generally discarded during text collection, and collecting it is not straigh... | D18-1099 | 10.18653/v1/D18-1099 | null | null | null |
D18-1100 | SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation | https://aclanthology.org/D18-1100/ | [
"Xinyi Wang",
"Hieu Pham",
"Zihang Dai",
"Graham Neubig"
] | In this work, we examine methods for data augmentation for text-based tasks such as neural machine translation (NMT). We formulate the design of a data augmentation policy with desirable properties as an optimization problem, and derive a generic analytic solution. This solution not only subsumes some existing augmenta... | D18-1100 | 10.18653/v1/D18-1100 | null | 1808.07512 | title_snapshot |
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