NAACL
Collection
Accepted papers for NAACL (Annual Conference of the North American Chapter of the Association for Computational Linguistics), one dataset per year. • 9 items • Updated
paper_id stringlengths 8 8 | title stringlengths 22 128 | paper_url stringlengths 34 34 | authors listlengths 1 13 | abstract large_stringlengths 292 1.68k | anthology_id stringlengths 8 8 | doi stringlengths 20 20 | award stringclasses 0
values | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 1
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N18-1001 | Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition | https://aclanthology.org/N18-1001/ | [
"Zhenghui Wang",
"Yanru Qu",
"Liheng Chen",
"Jian Shen",
"Weinan Zhang",
"Shaodian Zhang",
"Yimei Gao",
"Gen Gu",
"Ken Chen",
"Yong Yu"
] | We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining. Medical records which are written by clinicians from different specialties usually contain quite different terminologies and writing styles. The dif... | N18-1001 | 10.18653/v1/N18-1001 | null | 1804.09021 | title_snapshot |
N18-1002 | Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss | https://aclanthology.org/N18-1002/ | [
"Peng Xu",
"Denilson Barbosa"
] | The task of Fine-grained Entity Type Classification (FETC) consists of assigning types from a hierarchy to entity mentions in text. Existing methods rely on distant supervision and are thus susceptible to noisy labels that can be out-of-context or overly-specific for the training sentence. Previous methods that attempt... | N18-1002 | 10.18653/v1/N18-1002 | null | 1803.03378 | title_snapshot |
N18-1003 | Joint Bootstrapping Machines for High Confidence Relation Extraction | https://aclanthology.org/N18-1003/ | [
"Pankaj Gupta",
"Benjamin Roth",
"Hinrich Schütze"
] | Semi-supervised bootstrapping techniques for relationship extraction from text iteratively expand a set of initial seed instances. Due to the lack of labeled data, a key challenge in bootstrapping is semantic drift: if a false positive instance is added during an iteration, then all following iterations are contaminate... | N18-1003 | 10.18653/v1/N18-1003 | null | 1805.00254 | title_snapshot |
N18-1004 | A Deep Generative Model of Vowel Formant Typology | https://aclanthology.org/N18-1004/ | [
"Ryan Cotterell",
"Jason Eisner"
] | What makes some types of languages more probable than others? For instance, we know that almost all spoken languages contain the vowel phoneme /i/; why should that be? The field of linguistic typology seeks to answer these questions and, thereby, divine the mechanisms that underlie human language. In our work, we tackl... | N18-1004 | 10.18653/v1/N18-1004 | null | 1807.02745 | title_snapshot |
N18-1005 | Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages | https://aclanthology.org/N18-1005/ | [
"Katharina Kann",
"Jesus Manuel Mager Hois",
"Ivan Vladimir Meza-Ruiz",
"Hinrich Schütze"
] | Morphological segmentation for polysynthetic languages is challenging, because a word may consist of many individual morphemes and training data can be extremely scarce. Since neural sequence-to-sequence (seq2seq) models define the state of the art for morphological segmentation in high-resource settings and for (mostl... | N18-1005 | 10.18653/v1/N18-1005 | null | 1804.06024 | title_snapshot |
N18-1006 | Improving Character-Based Decoding Using Target-Side Morphological Information for Neural Machine Translation | https://aclanthology.org/N18-1006/ | [
"Peyman Passban",
"Qun Liu",
"Andy Way"
] | Recently, neural machine translation (NMT) has emerged as a powerful alternative to conventional statistical approaches. However, its performance drops considerably in the presence of morphologically rich languages (MRLs). Neural engines usually fail to tackle the large vocabulary and high out-of-vocabulary (OOV) word ... | N18-1006 | 10.18653/v1/N18-1006 | null | 1804.06506 | title_snapshot |
N18-1007 | Parsing Speech: a Neural Approach to Integrating Lexical and Acoustic-Prosodic Information | https://aclanthology.org/N18-1007/ | [
"Trang Tran",
"Shubham Toshniwal",
"Mohit Bansal",
"Kevin Gimpel",
"Karen Livescu",
"Mari Ostendorf"
] | In conversational speech, the acoustic signal provides cues that help listeners disambiguate difficult parses. For automatically parsing spoken utterances, we introduce a model that integrates transcribed text and acoustic-prosodic features using a convolutional neural network over energy and pitch trajectories coupled... | N18-1007 | 10.18653/v1/N18-1007 | null | 1704.07287 | title_snapshot |
N18-1008 | Tied Multitask Learning for Neural Speech Translation | https://aclanthology.org/N18-1008/ | [
"Antonios Anastasopoulos",
"David Chiang"
] | We explore multitask models for neural translation of speech, augmenting them in order to reflect two intuitive notions. First, we introduce a model where the second task decoder receives information from the decoder of the first task, since higher-level intermediate representations should provide useful information. S... | N18-1008 | 10.18653/v1/N18-1008 | null | 1802.06655 | title_snapshot |
N18-1009 | Please Clap: Modeling Applause in Campaign Speeches | https://aclanthology.org/N18-1009/ | [
"Jon Gillick",
"David Bamman"
] | This work examines the rhetorical techniques that speakers employ during political campaigns. We introduce a new corpus of speeches from campaign events in the months leading up to the 2016 U.S. presidential election and develop new models for predicting moments of audience applause. In contrast to existing datasets, w... | N18-1009 | 10.18653/v1/N18-1009 | null | null | null |
N18-1010 | Attentive Interaction Model: Modeling Changes in View in Argumentation | https://aclanthology.org/N18-1010/ | [
"Yohan Jo",
"Shivani Poddar",
"Byungsoo Jeon",
"Qinlan Shen",
"Carolyn Rosé",
"Graham Neubig"
] | We present a neural architecture for modeling argumentative dialogue that explicitly models the interplay between an Opinion Holder’s (OH’s) reasoning and a challenger’s argument, with the goal of predicting if the argument successfully changes the OH’s view. The model has two components: (1) vulnerable region detectio... | N18-1010 | 10.18653/v1/N18-1010 | null | 1804.00065 | title_snapshot |
N18-1011 | Automatic Focus Annotation: Bringing Formal Pragmatics Alive in Analyzing the Information Structure of Authentic Data | https://aclanthology.org/N18-1011/ | [
"Ramon Ziai",
"Detmar Meurers"
] | Analyzing language in context, both from a theoretical and from a computational perspective, is receiving increased interest. Complementing the research in linguistics on discourse and information structure, in computational linguistics identifying discourse concepts was also shown to improve the performance of certain... | N18-1011 | 10.18653/v1/N18-1011 | null | null | null |
N18-1012 | Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer | https://aclanthology.org/N18-1012/ | [
"Sudha Rao",
"Joel Tetreault"
] | Style transfer is the task of automatically transforming a piece of text in one particular style into another. A major barrier to progress in this field has been a lack of training and evaluation datasets, as well as benchmarks and automatic metrics. In this work, we create the largest corpus for a particular stylistic... | N18-1012 | 10.18653/v1/N18-1012 | null | 1803.06535 | title_snapshot |
N18-1013 | Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph | https://aclanthology.org/N18-1013/ | [
"Zeyu Dai",
"Ruihong Huang"
] | We argue that semantic meanings of a sentence or clause can not be interpreted independently from the rest of a paragraph, or independently from all discourse relations and the overall paragraph-level discourse structure. With the goal of improving implicit discourse relation classification, we introduce a paragraph-le... | N18-1013 | 10.18653/v1/N18-1013 | null | 1804.05918 | title_snapshot |
N18-1014 | A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation | https://aclanthology.org/N18-1014/ | [
"Juraj Juraska",
"Panagiotis Karagiannis",
"Kevin Bowden",
"Marilyn Walker"
] | Natural language generation lies at the core of generative dialogue systems and conversational agents. We describe an ensemble neural language generator, and present several novel methods for data representation and augmentation that yield improved results in our model. We test the model on three datasets in the restau... | N18-1014 | 10.18653/v1/N18-1014 | null | 1805.06553 | title_snapshot |
N18-1015 | A Melody-Conditioned Lyrics Language Model | https://aclanthology.org/N18-1015/ | [
"Kento Watanabe",
"Yuichiroh Matsubayashi",
"Satoru Fukayama",
"Masataka Goto",
"Kentaro Inui",
"Tomoyasu Nakano"
] | This paper presents a novel, data-driven language model that produces entire lyrics for a given input melody. Previously proposed models for lyrics generation suffer from the inability of capturing the relationship between lyrics and melody partly due to the unavailability of lyrics-melody aligned data. In this study, ... | N18-1015 | 10.18653/v1/N18-1015 | null | null | null |
N18-1016 | Discourse-Aware Neural Rewards for Coherent Text Generation | https://aclanthology.org/N18-1016/ | [
"Antoine Bosselut",
"Asli Celikyilmaz",
"Xiaodong He",
"Jianfeng Gao",
"Po-Sen Huang",
"Yejin Choi"
] | In this paper, we investigate the use of discourse-aware rewards with reinforcement learning to guide a model to generate long, coherent text. In particular, we propose to learn neural rewards to model cross-sentence ordering as a means to approximate desired discourse structure. Empirical results demonstrate that a ge... | N18-1016 | 10.18653/v1/N18-1016 | null | 1805.03766 | title_snapshot |
N18-1017 | Natural Answer Generation with Heterogeneous Memory | https://aclanthology.org/N18-1017/ | [
"Yao Fu",
"Yansong Feng"
] | Memory augmented encoder-decoder framework has achieved promising progress for natural language generation tasks. Such frameworks enable a decoder to retrieve from a memory during generation. However, less research has been done to take care of the memory contents from different sources, which are often of heterogeneou... | N18-1017 | 10.18653/v1/N18-1017 | null | null | null |
N18-1018 | Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation | https://aclanthology.org/N18-1018/ | [
"Shuming Ma",
"Xu Sun",
"Wei Li",
"Sujian Li",
"Wenjie Li",
"Xuancheng Ren"
] | Most recent approaches use the sequence-to-sequence model for paraphrase generation. The existing sequence-to-sequence model tends to memorize the words and the patterns in the training dataset instead of learning the meaning of the words. Therefore, the generated sentences are often grammatically correct but semantica... | N18-1018 | 10.18653/v1/N18-1018 | null | 1803.01465 | title_snapshot |
N18-1019 | Simplification Using Paraphrases and Context-Based Lexical Substitution | https://aclanthology.org/N18-1019/ | [
"Reno Kriz",
"Eleni Miltsakaki",
"Marianna Apidianaki",
"Chris Callison-Burch"
] | Lexical simplification involves identifying complex words or phrases that need to be simplified, and recommending simpler meaning-preserving substitutes that can be more easily understood. We propose a complex word identification (CWI) model that exploits both lexical and contextual features, and a simplification mecha... | N18-1019 | 10.18653/v1/N18-1019 | null | null | null |
N18-1020 | Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types | https://aclanthology.org/N18-1020/ | [
"Hady Elsahar",
"Christophe Gravier",
"Frederique Laforest"
] | We present a neural model for question generation from knowledge graphs triples in a “Zero-shot” setup, that is generating questions for predicate, subject types or object types that were not seen at training time. Our model leverages triples occurrences in the natural language corpus in a encoder-decoder architecture,... | N18-1020 | 10.18653/v1/N18-1020 | null | 1802.06842 | title_snapshot |
N18-1021 | Automated Essay Scoring in the Presence of Biased Ratings | https://aclanthology.org/N18-1021/ | [
"Evelin Amorim",
"Marcia Cançado",
"Adriano Veloso"
] | Studies in Social Sciences have revealed that when people evaluate someone else, their evaluations often reflect their biases. As a result, rater bias may introduce highly subjective factors that make their evaluations inaccurate. This may affect automated essay scoring models in many ways, as these models are typicall... | N18-1021 | 10.18653/v1/N18-1021 | null | null | null |
N18-1022 | Content-Based Citation Recommendation | https://aclanthology.org/N18-1022/ | [
"Chandra Bhagavatula",
"Sergey Feldman",
"Russell Power",
"Waleed Ammar"
] | We present a content-based method for recommending citations in an academic paper draft. We embed a given query document into a vector space, then use its nearest neighbors as candidates, and rerank the candidates using a discriminative model trained to distinguish between observed and unobserved citations. Unlike prev... | N18-1022 | 10.18653/v1/N18-1022 | null | 1802.08301 | title_snapshot |
N18-1023 | Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences | https://aclanthology.org/N18-1023/ | [
"Daniel Khashabi",
"Snigdha Chaturvedi",
"Michael Roth",
"Shyam Upadhyay",
"Dan Roth"
] | We present a reading comprehension challenge in which questions can only be answered by taking into account information from multiple sentences. We solicit and verify questions and answers for this challenge through a 4-step crowdsourcing experiment. Our challenge dataset contains 6,500+ questions for 1000+ paragraphs ... | N18-1023 | 10.18653/v1/N18-1023 | null | null | null |
N18-1024 | Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input | https://aclanthology.org/N18-1024/ | [
"Youmna Farag",
"Helen Yannakoudakis",
"Ted Briscoe"
] | We demonstrate that current state-of-the-art approaches to Automated Essay Scoring (AES) are not well-suited to capturing adversarially crafted input of grammatical but incoherent sequences of sentences. We develop a neural model of local coherence that can effectively learn connectedness features between sentences, an... | N18-1024 | 10.18653/v1/N18-1024 | null | 1804.06898 | title_snapshot |
N18-1025 | QuickEdit: Editing Text & Translations by Crossing Words Out | https://aclanthology.org/N18-1025/ | [
"David Grangier",
"Michael Auli"
] | We propose a framework for computer-assisted text editing. It applies to translation post-editing and to paraphrasing. Our proposal relies on very simple interactions: a human editor modifies a sentence by marking tokens they would like the system to change. Our model then generates a new sentence which reformulates th... | N18-1025 | 10.18653/v1/N18-1025 | null | 1711.04805 | title_snapshot |
N18-1026 | Tempo-Lexical Context Driven Word Embedding for Cross-Session Search Task Extraction | https://aclanthology.org/N18-1026/ | [
"Procheta Sen",
"Debasis Ganguly",
"Gareth Jones"
] | Task extraction is the process of identifying search intents over a set of queries potentially spanning multiple search sessions. Most existing research on task extraction has focused on identifying tasks within a single session, where the notion of a session is defined by a fixed length time window. By contrast, in th... | N18-1026 | 10.18653/v1/N18-1026 | null | null | null |
N18-1027 | Zero-Shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens | https://aclanthology.org/N18-1027/ | [
"Marek Rei",
"Anders Søgaard"
] | Can attention- or gradient-based visualization techniques be used to infer token-level labels for binary sequence tagging problems, using networks trained only on sentence-level labels? We construct a neural network architecture based on soft attention, train it as a binary sentence classifier and evaluate against toke... | N18-1027 | 10.18653/v1/N18-1027 | null | 1805.02214 | title_snapshot |
N18-1028 | Variable Typing: Assigning Meaning to Variables in Mathematical Text | https://aclanthology.org/N18-1028/ | [
"Yiannos Stathopoulos",
"Simon Baker",
"Marek Rei",
"Simone Teufel"
] | Information about the meaning of mathematical variables in text is useful in NLP/IR tasks such as symbol disambiguation, topic modeling and mathematical information retrieval (MIR). We introduce variable typing, the task of assigning one mathematical type (multi-word technical terms referring to mathematical concepts) ... | N18-1028 | 10.18653/v1/N18-1028 | null | null | null |
N18-1029 | Learning beyond Datasets: Knowledge Graph Augmented Neural Networks for Natural Language Processing | https://aclanthology.org/N18-1029/ | [
"Annervaz K M",
"Somnath Basu Roy Chowdhury",
"Ambedkar Dukkipati"
] | Machine Learning has been the quintessential solution for many AI problems, but learning models are heavily dependent on specific training data. Some learning models can be incorporated with prior knowledge using a Bayesian setup, but these learning models do not have the ability to access any organized world knowledge... | N18-1029 | 10.18653/v1/N18-1029 | null | 1802.05930 | title_snapshot |
N18-1030 | Comparing Constraints for Taxonomic Organization | https://aclanthology.org/N18-1030/ | [
"Anne Cocos",
"Marianna Apidianaki",
"Chris Callison-Burch"
] | Building a taxonomy from the ground up involves several sub-tasks: selecting terms to include, predicting semantic relations between terms, and selecting a subset of relational instances to keep, given constraints on the taxonomy graph. Methods for this final step – taxonomic organization – vary both in terms of the co... | N18-1030 | 10.18653/v1/N18-1030 | null | null | null |
N18-1031 | Improving Lexical Choice in Neural Machine Translation | https://aclanthology.org/N18-1031/ | [
"Toan Nguyen",
"David Chiang"
] | We explore two solutions to the problem of mistranslating rare words in neural machine translation. First, we argue that the standard output layer, which computes the inner product of a vector representing the context with all possible output word embeddings, rewards frequent words disproportionately, and we propose to... | N18-1031 | 10.18653/v1/N18-1031 | null | 1710.01329 | title_snapshot |
N18-1032 | Universal Neural Machine Translation for Extremely Low Resource Languages | https://aclanthology.org/N18-1032/ | [
"Jiatao Gu",
"Hany Hassan",
"Jacob Devlin",
"Victor O.K. Li"
] | In this paper, we propose a new universal machine translation approach focusing on languages with a limited amount of parallel data. Our proposed approach utilizes a transfer-learning approach to share lexical and sentence level representations across multiple source languages into one target language. The lexical part... | N18-1032 | 10.18653/v1/N18-1032 | null | 1802.05368 | title_snapshot |
N18-1033 | Classical Structured Prediction Losses for Sequence to Sequence Learning | https://aclanthology.org/N18-1033/ | [
"Sergey Edunov",
"Myle Ott",
"Michael Auli",
"David Grangier",
"Marc’Aurelio Ranzato"
] | There has been much recent work on training neural attention models at the sequence-level using either reinforcement learning-style methods or by optimizing the beam. In this paper, we survey a range of classical objective functions that have been widely used to train linear models for structured prediction and apply t... | N18-1033 | 10.18653/v1/N18-1033 | null | 1711.04956 | title_snapshot |
N18-1034 | Deep Dirichlet Multinomial Regression | https://aclanthology.org/N18-1034/ | [
"Adrian Benton",
"Mark Dredze"
] | Dirichlet Multinomial Regression (DMR) and other supervised topic models can incorporate arbitrary document-level features to inform topic priors. However, their ability to model corpora are limited by the representation and selection of these features – a choice the topic modeler must make. Instead, we seek models tha... | N18-1034 | 10.18653/v1/N18-1034 | null | null | null |
N18-1035 | Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse | https://aclanthology.org/N18-1035/ | [
"Xingshan Zeng",
"Jing Li",
"Lu Wang",
"Nicholas Beauchamp",
"Sarah Shugars",
"Kam-Fai Wong"
] | Millions of conversations are generated every day on social media platforms. With limited attention, it is challenging for users to select which discussions they would like to participate in. Here we propose a new method for microblog conversation recommendation. While much prior work has focused on post-level recommen... | N18-1035 | 10.18653/v1/N18-1035 | null | null | null |
N18-1036 | Before Name-Calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation | https://aclanthology.org/N18-1036/ | [
"Ivan Habernal",
"Henning Wachsmuth",
"Iryna Gurevych",
"Benno Stein"
] | Arguing without committing a fallacy is one of the main requirements of an ideal debate. But even when debating rules are strictly enforced and fallacious arguments punished, arguers often lapse into attacking the opponent by an ad hominem argument. As existing research lacks solid empirical investigation of the typolo... | N18-1036 | 10.18653/v1/N18-1036 | null | 1802.06613 | title_snapshot |
N18-1037 | Scene Graph Parsing as Dependency Parsing | https://aclanthology.org/N18-1037/ | [
"Yu-Siang Wang",
"Chenxi Liu",
"Xiaohui Zeng",
"Alan Yuille"
] | In this paper, we study the problem of parsing structured knowledge graphs from textual descriptions. In particular, we consider the scene graph representation that considers objects together with their attributes and relations: this representation has been proved useful across a variety of vision and language applicat... | N18-1037 | 10.18653/v1/N18-1037 | null | 1803.09189 | title_snapshot |
N18-1038 | Learning Visually Grounded Sentence Representations | https://aclanthology.org/N18-1038/ | [
"Douwe Kiela",
"Alexis Conneau",
"Allan Jabri",
"Maximilian Nickel"
] | We investigate grounded sentence representations, where we train a sentence encoder to predict the image features of a given caption—i.e., we try to “imagine” how a sentence would be depicted visually—and use the resultant features as sentence representations. We examine the quality of the learned representations on a ... | N18-1038 | 10.18653/v1/N18-1038 | null | 1707.06320 | title_snapshot |
N18-1039 | Comparatives, Quantifiers, Proportions: a Multi-Task Model for the Learning of Quantities from Vision | https://aclanthology.org/N18-1039/ | [
"Sandro Pezzelle",
"Ionut-Teodor Sorodoc",
"Raffaella Bernardi"
] | The present work investigates whether different quantification mechanisms (set comparison, vague quantification, and proportional estimation) can be jointly learned from visual scenes by a multi-task computational model. The motivation is that, in humans, these processes underlie the same cognitive, non-symbolic abilit... | N18-1039 | 10.18653/v1/N18-1039 | null | 1804.05018 | title_snapshot |
N18-1040 | Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets | https://aclanthology.org/N18-1040/ | [
"Wei-Lun Chao",
"Hexiang Hu",
"Fei Sha"
] | Visual question answering (Visual QA) has attracted a lot of attention lately, seen essentially as a form of (visual) Turing test that artificial intelligence should strive to achieve. In this paper, we study a crucial component of this task: how can we design good datasets for the task? We focus on the design of multi... | N18-1040 | 10.18653/v1/N18-1040 | null | 1704.07121 | title_snapshot |
N18-1041 | Abstract Meaning Representation for Paraphrase Detection | https://aclanthology.org/N18-1041/ | [
"Fuad Issa",
"Marco Damonte",
"Shay B. Cohen",
"Xiaohui Yan",
"Yi Chang"
] | Abstract Meaning Representation (AMR) parsing aims at abstracting away from the syntactic realization of a sentence, and denote only its meaning in a canonical form. As such, it is ideal for paraphrase detection, a problem in which one is required to specify whether two sentences have the same meaning. We show that naï... | N18-1041 | 10.18653/v1/N18-1041 | null | null | null |
N18-1042 | attr2vec: Jointly Learning Word and Contextual Attribute Embeddings with Factorization Machines | https://aclanthology.org/N18-1042/ | [
"Fabio Petroni",
"Vassilis Plachouras",
"Timothy Nugent",
"Jochen L. Leidner"
] | The widespread use of word embeddings is associated with the recent successes of many natural language processing (NLP) systems. The key approach of popular models such as word2vec and GloVe is to learn dense vector representations from the context of words. More recently, other approaches have been proposed that incor... | N18-1042 | 10.18653/v1/N18-1042 | null | null | null |
N18-1043 | Can Network Embedding of Distributional Thesaurus Be Combined with Word Vectors for Better Representation? | https://aclanthology.org/N18-1043/ | [
"Abhik Jana",
"Pawan Goyal"
] | Distributed representations of words learned from text have proved to be successful in various natural language processing tasks in recent times. While some methods represent words as vectors computed from text using predictive model (Word2vec) or dense count based model (GloVe), others attempt to represent these in a ... | N18-1043 | 10.18653/v1/N18-1043 | null | 1802.06196 | title_snapshot |
N18-1044 | Deep Neural Models of Semantic Shift | https://aclanthology.org/N18-1044/ | [
"Alex Rosenfeld",
"Katrin Erk"
] | Diachronic distributional models track changes in word use over time. In this paper, we propose a deep neural network diachronic distributional model. Instead of modeling lexical change via a time series as is done in previous work, we represent time as a continuous variable and model a word’s usage as a function of ti... | N18-1044 | 10.18653/v1/N18-1044 | null | null | null |
N18-1045 | Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection | https://aclanthology.org/N18-1045/ | [
"Haw-Shiuan Chang",
"Ziyun Wang",
"Luke Vilnis",
"Andrew McCallum"
] | Modeling hypernymy, such as poodle is-a dog, is an important generalization aid to many NLP tasks, such as entailment, relation extraction, and question answering. Supervised learning from labeled hypernym sources, such as WordNet, limits the coverage of these models, which can be addressed by learning hypernyms from u... | N18-1045 | 10.18653/v1/N18-1045 | null | 1710.00880 | title_snapshot |
N18-1046 | Mining Possessions: Existence, Type and Temporal Anchors | https://aclanthology.org/N18-1046/ | [
"Dhivya Chinnappa",
"Eduardo Blanco"
] | This paper presents a corpus and experiments to mine possession relations from text. Specifically, we target alienable and control possessions, and assign temporal anchors indicating when the possession holds between possessor and possessee. We present new annotations for this task, and experimental results using both ... | N18-1046 | 10.18653/v1/N18-1046 | null | null | null |
N18-1047 | Neural Tensor Networks with Diagonal Slice Matrices | https://aclanthology.org/N18-1047/ | [
"Takahiro Ishihara",
"Katsuhiko Hayashi",
"Hitoshi Manabe",
"Masashi Shimbo",
"Masaaki Nagata"
] | Although neural tensor networks (NTNs) have been successful in many NLP tasks, they require a large number of parameters to be estimated, which often leads to overfitting and a long training time. We address these issues by applying eigendecomposition to each slice matrix of a tensor to reduce its number of paramters. ... | N18-1047 | 10.18653/v1/N18-1047 | null | null | null |
N18-1048 | Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources | https://aclanthology.org/N18-1048/ | [
"Ivan Vulić",
"Goran Glavaš",
"Nikola Mrkšić",
"Anna Korhonen"
] | Word vector specialisation (also known as retrofitting) is a portable, light-weight approach to fine-tuning arbitrary distributional word vector spaces by injecting external knowledge from rich lexical resources such as WordNet. By design, these post-processing methods only update the vectors of words occurring in exte... | N18-1048 | 10.18653/v1/N18-1048 | null | 1805.03228 | title_snapshot |
N18-1049 | Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features | https://aclanthology.org/N18-1049/ | [
"Matteo Pagliardini",
"Prakhar Gupta",
"Martin Jaggi"
] | The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (i.e. semantic representations) of word sequences as well. We present a simple but efficient unsupervised objective to train distributed repr... | N18-1049 | 10.18653/v1/N18-1049 | null | 1703.02507 | title_snapshot |
N18-1050 | Learning Domain Representation for Multi-Domain Sentiment Classification | https://aclanthology.org/N18-1050/ | [
"Qi Liu",
"Yue Zhang",
"Jiangming Liu"
] | Training data for sentiment analysis are abundant in multiple domains, yet scarce for other domains. It is useful to leveraging data available for all existing domains to enhance performance on different domains. We investigate this problem by learning domain-specific representations of input sentences using neural net... | N18-1050 | 10.18653/v1/N18-1050 | null | null | null |
N18-1051 | Learning Sentence Representations over Tree Structures for Target-Dependent Classification | https://aclanthology.org/N18-1051/ | [
"Junwen Duan",
"Xiao Ding",
"Ting Liu"
] | Target-dependent classification tasks, such as aspect-level sentiment analysis, perform fine-grained classifications towards specific targets. Semantic compositions over tree structures are promising for such tasks, as they can potentially capture long-distance interactions between targets and their contexts. However, ... | N18-1051 | 10.18653/v1/N18-1051 | null | null | null |
N18-1052 | Relevant Emotion Ranking from Text Constrained with Emotion Relationships | https://aclanthology.org/N18-1052/ | [
"Deyu Zhou",
"Yang Yang",
"Yulan He"
] | Text might contain or invoke multiple emotions with varying intensities. As such, emotion detection, to predict multiple emotions associated with a given text, can be cast into a multi-label classification problem. We would like to go one step further so that a ranked list of relevant emotions are generated where top r... | N18-1052 | 10.18653/v1/N18-1052 | null | null | null |
N18-1053 | Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality | https://aclanthology.org/N18-1053/ | [
"Md Shad Akhtar",
"Palaash Sawant",
"Sukanta Sen",
"Asif Ekbal",
"Pushpak Bhattacharyya"
] | Efficient word representations play an important role in solving various problems related to Natural Language Processing (NLP), data mining, text mining etc. The issue of data sparsity poses a great challenge in creating efficient word representation model for solving the underlying problem. The problem is more intensi... | N18-1053 | 10.18653/v1/N18-1053 | null | null | null |
N18-1054 | SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning with Semantic Role Labeling | https://aclanthology.org/N18-1054/ | [
"Ana Marasović",
"Anette Frank"
] | For over a decade, machine learning has been used to extract opinion-holder-target structures from text to answer the question “Who expressed what kind of sentiment towards what?”. Recent neural approaches do not outperform the state-of-the-art feature-based models for Opinion Role Labeling (ORL). We suspect this is du... | N18-1054 | 10.18653/v1/N18-1054 | null | 1711.00768 | title_snapshot |
N18-1055 | Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task | https://aclanthology.org/N18-1055/ | [
"Marcin Junczys-Dowmunt",
"Roman Grundkiewicz",
"Shubha Guha",
"Kenneth Heafield"
] | Previously, neural methods in grammatical error correction (GEC) did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) baselines. We demonstrate parallels between neural GEC and low-resource neural MT and successfully adapt several methods from low-resource MT to neural G... | N18-1055 | 10.18653/v1/N18-1055 | null | 1804.05940 | title_snapshot |
N18-1056 | Robust Cross-Lingual Hypernymy Detection Using Dependency Context | https://aclanthology.org/N18-1056/ | [
"Shyam Upadhyay",
"Yogarshi Vyas",
"Marine Carpuat",
"Dan Roth"
] | Cross-lingual Hypernymy Detection involves determining if a word in one language (“fruit”) is a hypernym of a word in another language (“pomme” i.e. apple in French). The ability to detect hypernymy cross-lingually can aid in solving cross-lingual versions of tasks such as textual entailment and event coreference. We p... | N18-1056 | 10.18653/v1/N18-1056 | null | 1803.11291 | title_snapshot |
N18-1057 | Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction | https://aclanthology.org/N18-1057/ | [
"Ziang Xie",
"Guillaume Genthial",
"Stanley Xie",
"Andrew Ng",
"Dan Jurafsky"
] | Translation-based methods for grammar correction that directly map noisy, ungrammatical text to their clean counterparts are able to correct a broad range of errors; however, such techniques are bottlenecked by the need for a large parallel corpus of noisy and clean sentence pairs. In this paper, we consider synthesizi... | N18-1057 | 10.18653/v1/N18-1057 | null | null | null |
N18-1058 | Self-Training for Jointly Learning to Ask and Answer Questions | https://aclanthology.org/N18-1058/ | [
"Mrinmaya Sachan",
"Eric Xing"
] | Building curious machines that can answer as well as ask questions is an important challenge for AI. The two tasks of question answering and question generation are usually tackled separately in the NLP literature. At the same time, both require significant amounts of supervised data which is hard to obtain in many dom... | N18-1058 | 10.18653/v1/N18-1058 | null | null | null |
N18-1059 | The Web as a Knowledge-Base for Answering Complex Questions | https://aclanthology.org/N18-1059/ | [
"Alon Talmor",
"Jonathan Berant"
] | Answering complex questions is a time-consuming activity for humans that requires reasoning and integration of information. Recent work on reading comprehension made headway in answering simple questions, but tackling complex questions is still an ongoing research challenge. Conversely, semantic parsers have been succe... | N18-1059 | 10.18653/v1/N18-1059 | null | 1803.06643 | title_snapshot |
N18-1060 | A Meaning-Based Statistical English Math Word Problem Solver | https://aclanthology.org/N18-1060/ | [
"Chao-Chun Liang",
"Yu-Shiang Wong",
"Yi-Chung Lin",
"Keh-Yih Su"
] | We introduce MeSys, a meaning-based approach, for solving English math word problems (MWPs) via understanding and reasoning in this paper. It first analyzes the text, transforms both body and question parts into their corresponding logic forms, and then performs inference on them. The associated context of each quantit... | N18-1060 | 10.18653/v1/N18-1060 | null | 1803.06064 | title_snapshot |
N18-1061 | Fine-Grained Temporal Orientation and its Relationship with Psycho-Demographic Correlates | https://aclanthology.org/N18-1061/ | [
"Sabyasachi Kamila",
"Mohammed Hasanuzzaman",
"Asif Ekbal",
"Pushpak Bhattacharyya",
"Andy Way"
] | Temporal orientation refers to an individual’s tendency to connect to the psychological concepts of past, present or future, and it affects personality, motivation, emotion, decision making and stress coping processes. The study of the social media users’ psycho-demographic attributes from the perspective of human temp... | N18-1061 | 10.18653/v1/N18-1061 | null | null | null |
N18-1062 | Querying Word Embeddings for Similarity and Relatedness | https://aclanthology.org/N18-1062/ | [
"Fatemeh Torabi Asr",
"Robert Zinkov",
"Michael Jones"
] | Word embeddings obtained from neural network models such as Word2Vec Skipgram have become popular representations of word meaning and have been evaluated on a variety of word similarity and relatedness norming data. Skipgram generates a set of word and context embeddings, the latter typically discarded after training. ... | N18-1062 | 10.18653/v1/N18-1062 | null | null | null |
N18-1063 | Semantic Structural Evaluation for Text Simplification | https://aclanthology.org/N18-1063/ | [
"Elior Sulem",
"Omri Abend",
"Ari Rappoport"
] | Current measures for evaluating text simplification systems focus on evaluating lexical text aspects, neglecting its structural aspects. In this paper we propose the first measure to address structural aspects of text simplification, called SAMSA. It leverages recent advances in semantic parsing to assess simplificatio... | N18-1063 | 10.18653/v1/N18-1063 | null | 1810.05022 | title_snapshot |
N18-1064 | Entity Commonsense Representation for Neural Abstractive Summarization | https://aclanthology.org/N18-1064/ | [
"Reinald Kim Amplayo",
"Seonjae Lim",
"Seung-won Hwang"
] | A major proportion of a text summary includes important entities found in the original text. These entities build up the topic of the summary. Moreover, they hold commonsense information once they are linked to a knowledge base. Based on these observations, this paper investigates the usage of linked entities to guide ... | N18-1064 | 10.18653/v1/N18-1064 | null | 1806.05504 | title_snapshot |
N18-1065 | Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies | https://aclanthology.org/N18-1065/ | [
"Max Grusky",
"Mor Naaman",
"Yoav Artzi"
] | We present NEWSROOM, a summarization dataset of 1.3 million articles and summaries written by authors and editors in newsrooms of 38 major news publications. Extracted from search and social media metadata between 1998 and 2017, these high-quality summaries demonstrate high diversity of summarization styles. In particu... | N18-1065 | 10.18653/v1/N18-1065 | null | 1804.11283 | title_snapshot |
N18-1066 | Polyglot Semantic Parsing in APIs | https://aclanthology.org/N18-1066/ | [
"Kyle Richardson",
"Jonathan Berant",
"Jonas Kuhn"
] | Traditional approaches to semantic parsing (SP) work by training individual models for each available parallel dataset of text-meaning pairs. In this paper, we explore the idea of polyglot semantic translation, or learning semantic parsing models that are trained on multiple datasets and natural languages. In particula... | N18-1066 | 10.18653/v1/N18-1066 | null | 1803.06966 | title_snapshot |
N18-1067 | Neural Models of Factuality | https://aclanthology.org/N18-1067/ | [
"Rachel Rudinger",
"Aaron Steven White",
"Benjamin Van Durme"
] | We present two neural models for event factuality prediction, which yield significant performance gains over previous models on three event factuality datasets: FactBank, UW, and MEANTIME. We also present a substantial expansion of the It Happened portion of the Universal Decompositional Semantics dataset, yielding the... | N18-1067 | 10.18653/v1/N18-1067 | null | 1804.02472 | title_snapshot |
N18-1068 | Accurate Text-Enhanced Knowledge Graph Representation Learning | https://aclanthology.org/N18-1068/ | [
"Bo An",
"Bo Chen",
"Xianpei Han",
"Le Sun"
] | Previous representation learning techniques for knowledge graph representation usually represent the same entity or relation in different triples with the same representation, without considering the ambiguity of relations and entities. To appropriately handle the semantic variety of entities/relations in distinct trip... | N18-1068 | 10.18653/v1/N18-1068 | null | null | null |
N18-1069 | Acquisition of Phrase Correspondences Using Natural Deduction Proofs | https://aclanthology.org/N18-1069/ | [
"Hitomi Yanaka",
"Koji Mineshima",
"Pascual Martínez-Gómez",
"Daisuke Bekki"
] | How to identify, extract, and use phrasal knowledge is a crucial problem for the task of Recognizing Textual Entailment (RTE). To solve this problem, we propose a method for detecting paraphrases via natural deduction proofs of semantic relations between sentence pairs. Our solution relies on a graph reformulation of p... | N18-1069 | 10.18653/v1/N18-1069 | null | 1804.07656 | title_snapshot |
N18-1070 | Automatic Stance Detection Using End-to-End Memory Networks | https://aclanthology.org/N18-1070/ | [
"Mitra Mohtarami",
"Ramy Baly",
"James Glass",
"Preslav Nakov",
"Lluís Màrquez",
"Alessandro Moschitti"
] | We present an effective end-to-end memory network model that jointly (i) predicts whether a given document can be considered as relevant evidence for a given claim, and (ii) extracts snippets of evidence that can be used to reason about the factuality of the target claim. Our model combines the advantages of convolutio... | N18-1070 | 10.18653/v1/N18-1070 | null | 1804.07581 | title_snapshot |
N18-1071 | Collective Entity Disambiguation with Structured Gradient Tree Boosting | https://aclanthology.org/N18-1071/ | [
"Yi Yang",
"Ozan Irsoy",
"Kazi Shefaet Rahman"
] | We present a gradient-tree-boosting-based structured learning model for jointly disambiguating named entities in a document. Gradient tree boosting is a widely used machine learning algorithm that underlies many top-performing natural language processing systems. Surprisingly, most works limit the use of gradient tree ... | N18-1071 | 10.18653/v1/N18-1071 | null | 1802.10229 | title_snapshot |
N18-1072 | DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors | https://aclanthology.org/N18-1072/ | [
"Prodromos Kolyvakis",
"Alexandros Kalousis",
"Dimitris Kiritsis"
] | Ontologies compartmentalize types and relations in a target domain and provide the semantic backbone needed for a plethora of practical applications. Very often different ontologies are developed independently for the same domain. Such “parallel” ontologies raise the need for a process that will establish alignments be... | N18-1072 | 10.18653/v1/N18-1072 | null | null | null |
N18-1073 | Efficient Sequence Learning with Group Recurrent Networks | https://aclanthology.org/N18-1073/ | [
"Fei Gao",
"Lijun Wu",
"Li Zhao",
"Tao Qin",
"Xueqi Cheng",
"Tie-Yan Liu"
] | Recurrent neural networks have achieved state-of-the-art results in many artificial intelligence tasks, such as language modeling, neural machine translation, speech recognition and so on. One of the key factors to these successes is big models. However, training such big models usually takes days or even weeks of time... | N18-1073 | 10.18653/v1/N18-1073 | null | null | null |
N18-1074 | FEVER: a Large-scale Dataset for Fact Extraction and VERification | https://aclanthology.org/N18-1074/ | [
"James Thorne",
"Andreas Vlachos",
"Christos Christodoulopoulos",
"Arpit Mittal"
] | In this paper we introduce a new publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. It consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The cl... | N18-1074 | 10.18653/v1/N18-1074 | null | 1803.05355 | title_snapshot |
N18-1075 | Global Relation Embedding for Relation Extraction | https://aclanthology.org/N18-1075/ | [
"Yu Su",
"Honglei Liu",
"Semih Yavuz",
"Izzeddin Gür",
"Huan Sun",
"Xifeng Yan"
] | We study the problem of textual relation embedding with distant supervision. To combat the wrong labeling problem of distant supervision, we propose to embed textual relations with global statistics of relations, i.e., the co-occurrence statistics of textual and knowledge base relations collected from the entire corpus... | N18-1075 | 10.18653/v1/N18-1075 | null | 1704.05958 | title_snapshot |
N18-1076 | Implicit Argument Prediction with Event Knowledge | https://aclanthology.org/N18-1076/ | [
"Pengxiang Cheng",
"Katrin Erk"
] | Implicit arguments are not syntactically connected to their predicates, and are therefore hard to extract. Previous work has used models with large numbers of features, evaluated on very small datasets. We propose to train models for implicit argument prediction on a simple cloze task, for which data can be generated a... | N18-1076 | 10.18653/v1/N18-1076 | null | 1802.07226 | title_snapshot |
N18-1077 | Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource | https://aclanthology.org/N18-1077/ | [
"Qiang Ning",
"Hao Wu",
"Haoruo Peng",
"Dan Roth"
] | Extracting temporal relations (before, after, overlapping, etc.) is a key aspect of understanding events described in natural language. We argue that this task would gain from the availability of a resource that provides prior knowledge in the form of the temporal order that events usually follow. This paper develops s... | N18-1077 | 10.18653/v1/N18-1077 | null | 1804.06020 | title_snapshot |
N18-1078 | Multimodal Named Entity Recognition for Short Social Media Posts | https://aclanthology.org/N18-1078/ | [
"Seungwhan Moon",
"Leonardo Neves",
"Vitor Carvalho"
] | We introduce a new task called Multimodal Named Entity Recognition (MNER) for noisy user-generated data such as tweets or Snapchat captions, which comprise short text with accompanying images. These social media posts often come in inconsistent or incomplete syntax and lexical notations with very limited surrounding te... | N18-1078 | 10.18653/v1/N18-1078 | null | 1802.07862 | title_snapshot |
N18-1079 | Nested Named Entity Recognition Revisited | https://aclanthology.org/N18-1079/ | [
"Arzoo Katiyar",
"Claire Cardie"
] | We propose a novel recurrent neural network-based approach to simultaneously handle nested named entity recognition and nested entity mention detection. The model learns a hypergraph representation for nested entities using features extracted from a recurrent neural network. In evaluations on three standard data sets, ... | N18-1079 | 10.18653/v1/N18-1079 | null | null | null |
N18-1080 | Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction | https://aclanthology.org/N18-1080/ | [
"Patrick Verga",
"Emma Strubell",
"Andrew McCallum"
] | Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. This approach often does not consider interactions across mentions, requires redundant computation for each mention pair, and ignores relationships expressed across se... | N18-1080 | 10.18653/v1/N18-1080 | null | 1802.10569 | title_snapshot |
N18-1081 | Supervised Open Information Extraction | https://aclanthology.org/N18-1081/ | [
"Gabriel Stanovsky",
"Julian Michael",
"Luke Zettlemoyer",
"Ido Dagan"
] | We present data and methods that enable a supervised learning approach to Open Information Extraction (Open IE). Central to the approach is a novel formulation of Open IE as a sequence tagging problem, addressing challenges such as encoding multiple extractions for a predicate. We also develop a bi-LSTM transducer, ext... | N18-1081 | 10.18653/v1/N18-1081 | null | null | null |
N18-1082 | Embedding Syntax and Semantics of Prepositions via Tensor Decomposition | https://aclanthology.org/N18-1082/ | [
"Hongyu Gong",
"Suma Bhat",
"Pramod Viswanath"
] | Prepositions are among the most frequent words in English and play complex roles in the syntax and semantics of sentences. Not surprisingly, they pose well-known difficulties in automatic processing of sentences (prepositional attachment ambiguities and idiosyncratic uses in phrases). Existing methods on preposition re... | N18-1082 | 10.18653/v1/N18-1082 | null | 1805.09389 | title_snapshot |
N18-1083 | From Phonology to Syntax: Unsupervised Linguistic Typology at Different Levels with Language Embeddings | https://aclanthology.org/N18-1083/ | [
"Johannes Bjerva",
"Isabelle Augenstein"
] | A core part of linguistic typology is the classification of languages according to linguistic properties, such as those detailed in the World Atlas of Language Structure (WALS). Doing this manually is prohibitively time-consuming, which is in part evidenced by the fact that only 100 out of over 7,000 languages spoken i... | N18-1083 | 10.18653/v1/N18-1083 | null | 1802.09375 | title_snapshot |
N18-1084 | Monte Carlo Syntax Marginals for Exploring and Using Dependency Parses | https://aclanthology.org/N18-1084/ | [
"Katherine A. Keith",
"Su Lin Blodgett",
"Brendan O’Connor"
] | Dependency parsing research, which has made significant gains in recent years, typically focuses on improving the accuracy of single-tree predictions. However, ambiguity is inherent to natural language syntax, and communicating such ambiguity is important for error analysis and better-informed downstream applications. ... | N18-1084 | 10.18653/v1/N18-1084 | null | 1804.06004 | title_snapshot |
N18-1085 | Neural Particle Smoothing for Sampling from Conditional Sequence Models | https://aclanthology.org/N18-1085/ | [
"Chu-Cheng Lin",
"Jason Eisner"
] | We introduce neural particle smoothing, a sequential Monte Carlo method for sampling annotations of an input string from a given probability model. In contrast to conventional particle filtering algorithms, we train a proposal distribution that looks ahead to the end of the input string by means of a right-to-left LSTM... | N18-1085 | 10.18653/v1/N18-1085 | null | 1804.10747 | title_snapshot |
N18-1086 | Neural Syntactic Generative Models with Exact Marginalization | https://aclanthology.org/N18-1086/ | [
"Jan Buys",
"Phil Blunsom"
] | We present neural syntactic generative models with exact marginalization that support both dependency parsing and language modeling. Exact marginalization is made tractable through dynamic programming over shift-reduce parsing and minimal RNN-based feature sets. Our algorithms complement previous approaches by supporti... | N18-1086 | 10.18653/v1/N18-1086 | null | null | null |
N18-1087 | Noise-Robust Morphological Disambiguation for Dialectal Arabic | https://aclanthology.org/N18-1087/ | [
"Nasser Zalmout",
"Alexander Erdmann",
"Nizar Habash"
] | User-generated text tends to be noisy with many lexical and orthographic inconsistencies, making natural language processing (NLP) tasks more challenging. The challenging nature of noisy text processing is exacerbated for dialectal content, where in addition to spelling and lexical differences, dialectal text is charac... | N18-1087 | 10.18653/v1/N18-1087 | null | null | null |
N18-1088 | Parsing Tweets into Universal Dependencies | https://aclanthology.org/N18-1088/ | [
"Yijia Liu",
"Yi Zhu",
"Wanxiang Che",
"Bing Qin",
"Nathan Schneider",
"Noah A. Smith"
] | We study the problem of analyzing tweets with universal dependencies (UD). We extend the UD guidelines to cover special constructions in tweets that affect tokenization, part-of-speech tagging, and labeled dependencies. Using the extended guidelines, we create a new tweet treebank for English (Tweebank v2) that is four... | N18-1088 | 10.18653/v1/N18-1088 | null | 1804.08228 | title_snapshot |
N18-1089 | Robust Multilingual Part-of-Speech Tagging via Adversarial Training | https://aclanthology.org/N18-1089/ | [
"Michihiro Yasunaga",
"Jungo Kasai",
"Dragomir Radev"
] | Adversarial training (AT) is a powerful regularization method for neural networks, aiming to achieve robustness to input perturbations. Yet, the specific effects of the robustness obtained from AT are still unclear in the context of natural language processing. In this paper, we propose and analyze a neural POS tagging... | N18-1089 | 10.18653/v1/N18-1089 | null | 1711.04903 | title_snapshot |
N18-1090 | Universal Dependency Parsing for Hindi-English Code-Switching | https://aclanthology.org/N18-1090/ | [
"Irshad Bhat",
"Riyaz A. Bhat",
"Manish Shrivastava",
"Dipti Sharma"
] | Code-switching is a phenomenon of mixing grammatical structures of two or more languages under varied social constraints. The code-switching data differ so radically from the benchmark corpora used in NLP community that the application of standard technologies to these data degrades their performance sharply. Unlike st... | N18-1090 | 10.18653/v1/N18-1090 | null | 1804.05868 | title_snapshot |
N18-1091 | What’s Going On in Neural Constituency Parsers? An Analysis | https://aclanthology.org/N18-1091/ | [
"David Gaddy",
"Mitchell Stern",
"Dan Klein"
] | A number of differences have emerged between modern and classic approaches to constituency parsing in recent years, with structural components like grammars and feature-rich lexicons becoming less central while recurrent neural network representations rise in popularity. The goal of this work is to analyze the extent t... | N18-1091 | 10.18653/v1/N18-1091 | null | 1804.07853 | title_snapshot |
N18-1092 | Deep Generative Model for Joint Alignment and Word Representation | https://aclanthology.org/N18-1092/ | [
"Miguel Rios",
"Wilker Aziz",
"Khalil Sima’an"
] | This work exploits translation data as a source of semantically relevant learning signal for models of word representation. In particular, we exploit equivalence through translation as a form of distributional context and jointly learn how to embed and align with a deep generative model. Our EmbedAlign model embeds wor... | N18-1092 | 10.18653/v1/N18-1092 | null | 1802.05883 | title_snapshot |
N18-1093 | Learning Word Embeddings for Low-Resource Languages by PU Learning | https://aclanthology.org/N18-1093/ | [
"Chao Jiang",
"Hsiang-Fu Yu",
"Cho-Jui Hsieh",
"Kai-Wei Chang"
] | Word embedding is a key component in many downstream applications in processing natural languages. Existing approaches often assume the existence of a large collection of text for learning effective word embedding. However, such a corpus may not be available for some low-resource languages. In this paper, we study how ... | N18-1093 | 10.18653/v1/N18-1093 | null | null | null |
N18-1094 | Exploring the Role of Prior Beliefs for Argument Persuasion | https://aclanthology.org/N18-1094/ | [
"Esin Durmus",
"Claire Cardie"
] | Public debate forums provide a common platform for exchanging opinions on a topic of interest. While recent studies in natural language processing (NLP) have provided empirical evidence that the language of the debaters and their patterns of interaction play a key role in changing the mind of a reader, research in psyc... | N18-1094 | 10.18653/v1/N18-1094 | null | 1906.11301 | title_snapshot |
N18-1095 | Inducing a Lexicon of Abusive Words – a Feature-Based Approach | https://aclanthology.org/N18-1095/ | [
"Michael Wiegand",
"Josef Ruppenhofer",
"Anna Schmidt",
"Clayton Greenberg"
] | We address the detection of abusive words. The task is to identify such words among a set of negative polar expressions. We propose novel features employing information from both corpora and lexical resources. These features are calibrated on a small manually annotated base lexicon which we use to produce a large lexic... | N18-1095 | 10.18653/v1/N18-1095 | null | null | null |
N18-1096 | Author Commitment and Social Power: Automatic Belief Tagging to Infer the Social Context of Interactions | https://aclanthology.org/N18-1096/ | [
"Vinodkumar Prabhakaran",
"Premkumar Ganeshkumar",
"Owen Rambow"
] | Understanding how social power structures affect the way we interact with one another is of great interest to social scientists who want to answer fundamental questions about human behavior, as well as to computer scientists who want to build automatic methods to infer the social contexts of interactions. In this paper... | N18-1096 | 10.18653/v1/N18-1096 | null | 1805.06016 | title_snapshot |
N18-1097 | Comparing Automatic and Human Evaluation of Local Explanations for Text Classification | https://aclanthology.org/N18-1097/ | [
"Dong Nguyen"
] | Text classification models are becoming increasingly complex and opaque, however for many applications it is essential that the models are interpretable. Recently, a variety of approaches have been proposed for generating local explanations. While robust evaluations are needed to drive further progress, so far it is un... | N18-1097 | 10.18653/v1/N18-1097 | null | null | null |
N18-1098 | Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time | https://aclanthology.org/N18-1098/ | [
"Pankaj Gupta",
"Subburam Rajaram",
"Hinrich Schütze",
"Bernt Andrassy"
] | Dynamic topic modeling facilitates the identification of topical trends over time in temporal collections of unstructured documents. We introduce a novel unsupervised neural dynamic topic model named as Recurrent Neural Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each time influence the to... | N18-1098 | 10.18653/v1/N18-1098 | null | 1711.05626 | title_snapshot |
N18-1099 | Lessons from the Bible on Modern Topics: Low-Resource Multilingual Topic Model Evaluation | https://aclanthology.org/N18-1099/ | [
"Shudong Hao",
"Jordan Boyd-Graber",
"Michael J. Paul"
] | Multilingual topic models enable document analysis across languages through coherent multilingual summaries of the data. However, there is no standard and effective metric to evaluate the quality of multilingual topics. We introduce a new intrinsic evaluation of multilingual topic models that correlates well with human... | N18-1099 | 10.18653/v1/N18-1099 | null | 1804.10184 | title_snapshot |
N18-1100 | Explainable Prediction of Medical Codes from Clinical Text | https://aclanthology.org/N18-1100/ | [
"James Mullenbach",
"Sarah Wiegreffe",
"Jon Duke",
"Jimeng Sun",
"Jacob Eisenstein"
] | Clinical notes are text documents that are created by clinicians for each patient encounter. They are typically accompanied by medical codes, which describe the diagnosis and treatment. Annotating these codes is labor intensive and error prone; furthermore, the connection between the codes and the text is not annotated... | N18-1100 | 10.18653/v1/N18-1100 | null | 1802.05695 | title_snapshot |
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