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Viewing 66 papers from 2018
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    • NeurIPS 2018
      Yexiang Xue, Yang Yuan, Zhitian Xu, Ashish Sabharwal
      Neural models operating over structured spaces such as knowledge graphs require a continuous embedding of the discrete elements of this space (such as entities) as well as the relationships between them. Relational embeddings with high expressivity, however, have high model complexity, making them…  (More)
    • NeurIPS 2018
      Roei Herzig, Moshiko Raboh, Gal Chechik, Jonathan Berant, Amir Globerson
      Machine understanding of complex images is a key goal of artificial intelligence. One challenge underlying this task is that visual scenes contain multiple inter-related objects, and that global context plays an important role in interpreting the scene. A natural modeling framework for capturing…  (More)
    • NeurIPS 2018
      Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc Le, Ni Lao
      This paper presents Memory Augmented Policy Optimization (MAPO): a novel policy optimization formulation that incorporates a memory buffer of promising trajectories to reduce the variance of policy gradient estimates for deterministic environments with discrete actions. The formulation expresses…  (More)
    • EMNLP • Workshop: Analyzing and interpreting neural networks for NLP 2018
      Alon Jacovi, Oren Sar Shalom, Yoav Goldberg
      We present an analysis into the inner workings of Convolutional Neural Networks (CNNs) for processing text. CNNs used for computer vision can be interpreted by projecting filters into image space, but for discrete sequence inputs CNNs remain a mystery. We aim to understand the method by which the…  (More)
    • EMNLP 2018
      Yang Liu, Matt Gardner, Mirella Lapata
      Many tasks in natural language processing involve comparing two sentences to compute some notion of relevance, entailment, or similarity. Typically this comparison is done either at the word level or at the sentence level, with no attempt to leverage the inherent structure of the sentence. When…  (More)
    • EMNLP 2018
      Gabriel Stanovsky, Mark Hopkins
      We propose Odd-Man-Out, a novel task which aims to test different properties of word representations. An Odd-Man-Out puzzle is composed of 5 (or more) words, and requires the system to choose the one which does not belong with the others. We show that this simple setup is capable of teasing out…  (More)
    • EMNLP 2018
      Jonathan Herzig, Jonathan Berant
      Building a semantic parser quickly in a new domain is a fundamental challenge for conversational interfaces, as current semantic parsers require expensive supervision and lack the ability to generalize to new domains. In this paper, we introduce a zero-shot approach to semantic parsing that can…  (More)
    • EMNLP • Workshop: Analyzing and interpreting neural networks for NLP 2018
      Shauli Ravfogel, Francis M. Tyers, Yoav Goldberg
      Sequential neural networks models are powerful tools in a variety of Natural Language Processing (NLP) tasks. The sequential nature of these models raises the questions: to what extent can these models implicitly learn hierarchical structures typical to human language, and what kind of grammatical…  (More)
    • EMNLP 2018
      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…  (More)
    • EMNLP 2018
      Asaf Amrami, Yoav Goldberg
      An established method for Word Sense Induction (WSI) uses a language model to predict probable substitutes for target words, and induces senses by clustering these resulting substitute vectors. We replace the ngram-based language model (LM) with a recurrent one. Beyond being more accurate, the use…  (More)
    • EMNLP 2018
      Todor Mihaylov, Peter Clark, Tushar Khot, Ashish Sabharwal
      We present a new kind of question answering dataset, OpenBookQA, modeled after open book exams for assessing human understanding of a subject. The open book that comes with our questions is a set of 1329 elementary level science facts. Roughly 6000 questions probe an understanding of these facts…  (More)
    • EMNLP 2018
      Niket Tandon, Bhavana Dalvi Mishra, 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…  (More)
    • EMNLP 2018
      Ge Gao, Eunsol Choi, Yejin Choi and 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…  (More)
    • EMNLP 2018
      Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang and Luke Zettlemoyer
      We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2…  (More)
    • EMNLP 2018
      Rowan Zellers, Yonatan Bisk, Roy Schwartz, and 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…  (More)
    • EMNLP 2018
      Dipendra Misra, Ming-Wei Chang, Xiaodong He, Wen-tau Yih
      Semantic parsing from denotations faces two key challenges in model training: (1) given only the denotations (e.g., answers), search for good candidate semantic parses, and (2) choose the best model update algorithm. We propose effective and general solutions to each of them. Using policy shaping…  (More)
    • EMNLP 2018
      Dongyeop Kang, Tushar Khot, Ashish Sabharwal and Peter Clark
      Most textual entailment models focus on lexical gaps between the premise text and the hypothesis, but rarely on knowledge gaps. We focus on filling these knowledge gaps in the Science Entailment task, by leveraging an external structured knowledge base (KB) of science facts. Our new architecture…  (More)
    • EMNLP 2018
      Hao Peng, Roy Schwartz, Sam Thomson, and Noah A. Smith
      Despite the tremendous empirical success of neural models in natural language processing, many of them lack the strong intuitions that accompany classical machine learning approaches. Recently, connections have been shown between convolutional neural networks (CNNs) and weighted finite state…  (More)
    • EMNLP 2018
      Swabha Swayamdipta, Sam Thomson, Kenton Lee, Luke Zettlemoyer, Chris Dyer, and Noah A. Smith
      We introduce the syntactic scaffold, an approach to incorporating syntactic information into semantic tasks. Syntactic scaffolds avoid expensive syntactic processing at runtime, only making use of a treebank during training, through a multitask objective. We improve over strong baselines on…  (More)
    • EMNLP 2018
      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…  (More)
    • EMNLP 2018
      Matthew Peters, Mark Neumann, Wen-tau Yih, and Luke Zettlemoyer
      Contextual word representations derived from pre-trained bidirectional language models (biLMs) have recently been shown to provide significant improvements to the state of the art for a wide range of NLP tasks. However, many questions remain as to how and why these models are so effective. In this…  (More)
    • EMNLP 2018
      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 on this benchmark…  (More)
    • UAI 2018
      Ashish Sabharwal, Yexiang Xue
      We propose a new algorithm for computing a constant-factor approximation of precision-recall (PR) curves for massive noisy datasets produced by generative models. Assessing validity of items in such datasets requires human annotation, which is costly and must be minimized. Our algorithm, AdaStrat…  (More)
    • ArXiv 2018
      Sergey Feldman, Kyle Lo, Waleed Ammar
      We explore the degree to which papers prepublished on arXiv garner more citations, in an attempt to paint a sharper picture of fairness issues related to prepublishing. A paper’s citation count is estimated using a negative-binomial generalized linear model (GLM) while observing a binary variable…  (More)
    • NAACL-HLT 2018
      Waleed Ammar, Dirk Groeneveld, Chandra Bhagavatula, Iz Beltagy, Miles Crawford, Doug Downey, Jason Dunkelberger, Ahmed Elgohary, Sergey Feldman, Vu Ha, Rodney Kinney, Sebastian Kohlmeier, Kyle Lo, Tyler Murray, Hsu-Han Ooi, Matthew E. Peters, et al.
      We describe a deployed scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery. The resulting literature graph consists of more than 280M nodes, representing papers, authors, entities and various interactions…  (More)
    • ACL 2018
      Vidur Joshi, Matthew Peters, and Mark Hopkins
      We revisit domain adaptation for parsers in the neural era. First we show that recent advances in word representations greatly diminish the need for domain adaptation when the target domain is syntactically similar to the source domain. As evidence, we train a parser on the Wall Street Jour- nal…  (More)
    • ACL • NLP OSS Workshop 2018
      Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson Liu, Matthew Peters, Michael Schmitz, Luke Zettlemoyer
      This paper describes AllenNLP, a platform for research on deep learning methods in natural language understanding. AllenNLP is designed to support researchers who want to build novel language understanding models quickly and easily. It is built on top of PyTorch, allowing for dynamic computation…  (More)
    • ACL 2018
      Maarten Sap, Hannah Rashkin, Emily Allaway, Noah A. Smith and Yejin Choi
      We investigate a new commonsense inference task: given an event described in a short free-form text (“X drinks coffee in the morning”), a system reasons about the likely intents (“X wants to stay awake”) and reactions (“X feels alert”) of the event’s participants. To support this study, we…  (More)
    • ACL 2018
      Eunsol Choi, Omer Levy, Yejin Choi and Luke Zettlemoyer
      We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formulation allows us to use a new type of distant supervision at…  (More)
    • ACL 2018
      Ari Holtzman, Jan Buys, Maxwell Forbes, Antoine Bosselut, David Golub and Yejin Choi
      Despite their local fluency, long-form text generated from RNNs is often generic, repetitive, and even self-contradictory. We propose a unified learning framework that collectively addresses all the above issues by composing a committee of discriminators that can guide a base RNN generator towards…  (More)
    • ACL 2018
      Hannah Rashkin, Antoine Bosselut, Maarten Sap, Kevin Knight and Yejin Choi
      Understanding a narrative requires reading between the lines and reasoning about the unspoken but obvious implications about events and people’s mental states — a capability that is trivial for humans but remarkably hard for machines. To facilitate research addressing this challenge, we introduce a…  (More)
    • Award Best Paper Award
      ACL • RepL4NLP Workshop 2018
      Nelson F. Liu, Omer Levy, Roy Schwartz, Chenhao Tan, Noah A. Smith
      While recurrent neural networks have found success in a variety of natural language processing applications, they are general models of sequential data. We investigate how the properties of natural language data affect an LSTM's ability to learn a nonlinguistic task: recalling elements from its…  (More)
    • ACL 2018
      Christopher Clark and Matt Gardner
      We consider the problem of adapting neural paragraph-level question answering models to the case where entire documents are given as input. Our proposed solution trains models to produce well calibrated confidence scores for their results on individual paragraphs. We sample multiple paragraphs from…  (More)
    • CVPR 2018 Video
      Daniel Gordon, Aniruddha Kembhavi, Mohammad Rastegari, Joseph Redmon, Dieter Fox, Ali Farhadi
      We introduce Interactive Question Answering (IQA), the task of answering questions that require an autonomous agent to interact with a dynamic visual environment. IQA presents the agent with a scene and a question, like: “Are there any apples in the fridge?” The agent must navigate around the scene…  (More)
    • CVPR 2018
      Aishwarya Agrawal, Dhruv Batra, Devi Parikh, Aniruddha Kembhavi
      A number of studies have found that today’s Visual Question Answering (VQA) models are heavily driven by superficial correlations in the training data and lack sufficient image grounding. To encourage development of models geared towards the latter, we propose a new setting for VQA where for every…  (More)
    • ACL 2018
      Tushar Khot, Ashish Sabharwal and Dongyeop Kang
      We consider the problem of learning textual entailment models with limited supervision (5K-10K training examples), and present two complementary approaches for it. First, we propose knowledge-guided adversarial example generators for incorporating large lexical resources in entailment models via…  (More)
    • CVPR 2018
      Gunnar Sigurdsson, Cordelia Schmid, Ali Farhadi, Abhinav Gupta, Karteek Alahari
      Several theories in cognitive neuroscience suggest that when people interact with the world, or simulate interactions, they do so from a first-person egocentric perspective, and seamlessly transfer knowledge between third-person (observer) and first-person (actor). Despite this, learning such…  (More)
    • ECCV 2018
      Sachin Mehta, Mohammad Rastegari, Anat Caspi, Linda Shapiro, and Hannaneh Hajishirzi
      We introduce a fast and efficient convolutional neural network, ESPNet, for semantic segmentation of high resolution images under resource constraints. ESPNet is based on a new convolutional module, efficient spatial pyramid (ESP), which is efficient in terms of computation, memory, and power…  (More)
    • ECCV 2018
      Krishna Kumar Singh, Santosh Kumar Divvala, Ali Farhadi, and Yong Jae Lee
      We propose the idea of transferring common-sense knowledge from source categories to target categories for scalable object detection. In our setting, the training data for the source categories have bounding box annotations, while those for the target categories only have image-level annotations…  (More)
    • ECCV 2018 Video
      Tanmay Gupta, Dustin Schwenk, Ali Farhadi, Derek Hoiem, and Aniruddha Kembhavi
      Imagining a scene described in natural language with realistic layout and appearance of entities is the ultimate test of spatial, visual, and semantic world knowledge. Towards this goal, we present the Composition, Retrieval and Fusion Network (Craft), a model capable of learning this knowledge…  (More)
    • Award Best Paper Award
      NAACL 2018
      Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer
      We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states…  (More)
    • NAACL-HLT 2018
      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…  (More)
    • NAACL 2018
      Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Sam Bowman and Noah A. Smith
      Large-scale datasets for natural language inference are created by presenting crowd workers with a sentence (premise), and asking them to generate three new sentences (hypotheses) that it entails, contradicts, or is logically neutral with respect to. We show that, in a significant portion of such…  (More)
    • NAACL 2018
      Bhavana Dalvi, Lifu Huang, Niket Tandon, Wen-tau Yih, Peter Clark
      We present a new dataset and models for comprehending paragraphs about processes (e.g., photosynthesis), an important genre of text describing a dynamic world. The new dataset, ProPara, is the first to contain natural (rather than machine-generated) text about a changing world along with a full…  (More)
    • arXiv 2018
      Peter Clark, Bhavana Dalvi, Niket Tandon
      Our goal is to answer questions about paragraphs describing processes (e.g., photosynthesis). Texts of this genre are challenging because the effects of actions are often implicit (unstated), requiring background knowledge and inference to reason about the changing world states. To supply this…  (More)
    • NAACL-HLT 2018 Dataset
      Dongyeop Kang, Waleed Ammar, Bhavana Dalvi Mishra, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy Schwartz
      Peer reviewing is a central component in the scientific publishing process. We present the first public dataset of scientific peer reviews available for research pur- poses (PeerRead v1), providing an opportunity to study this important artifact. The dataset consists of 14.7K paper drafts and the…  (More)
    • ACL 2018
      Roy Schwartz, Sam Thomson and Noah A. Smith
      Recurrent and convolutional neural networks comprise two distinct families of models that have proven to be useful for encoding natural language utterances. In this paper we present SoPa, a new model that aims to bridge these two approaches. SoPa combines neural representation learning with…  (More)
    • CVPR 2018
      Jonghyun Choi, Jayant Krishnamurthy, Aniruddha Kembhavi, Ali Farhadi
      Diagrams often depict complex phenomena and serve as a good test bed for visual and textual reasoning. However, understanding diagrams using natural image understanding approaches requires large training datasets of diagrams, which are very hard to obtain. Instead, this can be addressed as a…  (More)
    • CVPR 2018
      Kiana Ehsani, Hessam Bagherinezhad, Joe Redmon, Roozbeh Mottaghi, Ali Farhadi
      We study the task of directly modelling a visually intelligent agent. Computer vision typically focuses on solving various subtasks related to visual intelligence. We depart from this standard approach to computer vision; instead we directly model a visually intelligent agent. Our model takes…  (More)
    • CVPR 2018
      Kiana Ehsani, Roozbeh Mottaghi, Ali Farhadi
      Objects often occlude each other in scenes; Inferring their appearance beyond their visible parts plays an important role in scene understanding, depth estimation, object interaction and manipulation. In this paper, we study the challenging problem of completing the appearance of occluded objects…  (More)
    • CVPR 2018
      Rowan Zellers, Mark Yatskar, Sam Thomson, Yejin Choi
      We investigate the problem of producing structured graph representations of visual scenes. Our work analyzes the role of motifs: regularly appearing substructures in scene graphs. We present new quantitative insights on such repeated structures in the Visual Genome dataset. Our analysis shows that…  (More)
    • NAACL 2018
      Po-Sen Huang, Chenglong Wang, Rishabh Singh, Wen-tau Yih, Xiaodong He
      In conventional supervised training, a model is trained to fit all the training examples. However, having a monolithic model may not always be the best strategy, as examples could vary widely. In this work, we explore a different learning protocol that treats each example as a unique pseudo-task…  (More)
    • NAACL 2018
      Asli Celikyilmaz, Antoine Bosselut, Xiaodong He and Yejin Choi
      We present deep communicating agents in an encoder-decoder architecture to address the challenges of representing a long document for abstractive summarization. With deep communicating agents, the task of encoding a long text is divided across multiple collaborating agents, each in charge of a…  (More)
    • NAACL 2018
      Antoine Bosselut, Asli Celikyilmaz, Xiaodong He, Jianfeng Gao, Po-Sen Huang and 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…  (More)
    • NAACL 2018
      Marjan Ghazvininejad, Yejin Choi and Kevin Knight
      We present the first neural poetry translation system. Unlike previous works that often fail to produce any translation for fixed rhyme and rhythm patterns, our system always translates a source text to an English poem. Human evaluation ranks translation quality as acceptable 78.2% of the time.
    • WSDM 2018
      Sreyasi Nag Chowdhury, Niket Tandon, Hakan Ferhatosmanoglu, Gerhard Weikum
      The social media explosion has populated the Internet with a wealth of images. There are two existing paradigms for image retrieval: 1)content-based image retrieval (BIR), which has traditionally used visual features for similarity search (e.g., SIFT features), and 2) tag-based image retrieval…  (More)
    • JCDL 2018
      Noah Siegel, Nicholas Lourie, Russell Power and Waleed Ammar
      Non-textual components such as charts, diagrams and tables provide key information in many scientific documents, but the lack of large labeled datasets has impeded the development of data-driven methods for scientific figure extraction. In this paper, we induce high-quality training labels for the…  (More)
    • ACL • Proceedings of the BioNLP 2018 Workshop 2018
      Lucy L. Wang, Chandra Bhagavatula, M. Neumann, Kyle Lo, Chris Wilhelm, Waleed Ammar
      Ontology alignment is the task of identifying semantically equivalent entities from two given ontologies. Different ontologies have different representations of the same entity, resulting in a need to de-duplicate entities when merging ontologies. We propose a method for enriching entities in an…  (More)
    • NAACL-HTL 2018
      Hao Fang, Hao Cheng, Maarten Sap, Elizabeth Clark, Ari Holtzman, Yejin Choi, Noah A. Smith, and Mari Ostendorf
      We present Sounding Board, a social chatbot that won the 2017 Amazon Alexa Prize. The system architecture consists of several components including spoken language processing, dialogue management, language generation, and content management, with emphasis on user-centric and content-driven design…  (More)
    • ICLR 2018 Podcast
      Antoine Bosselut, Omer Levy, Ari Holtzman, Corin Ennis, Dieter Fox, and Yejin Choi
      Understanding procedural language requires anticipating the causal effects of actions, even when they are not explicitly stated. In this work, we introduce Neural Process Networks to understand procedural text through (neural) simulation of action dynamics. Our model complements existing memory…  (More)
    • TACL 2018
      Hanie Sedghi and Ashish Sabharwal
      Given a knowledge base or KB containing (noisy) facts about common nouns or generics, such as "all trees produce oxygen" or "some animals live in forests", we consider the problem of inferring additional such facts at a precision similar to that of the starting KB. Such KBs capture general…  (More)
    • arXiv 2018
      Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, Oyvind Tafjord
      We present a new question set, text corpus, and baselines assembled to encourage AI research in advanced question answering. Together, these constitute the AI2 Reasoning Challenge (ARC), which requires far more powerful knowledge and reasoning than previous challenges such as SQuAD or SNLI. The ARC…  (More)
    • AAAI 2018
      Tushar Khot, Ashish Sabharwal, and Peter Clark
      We present a new dataset and model for textual entailment, derived from treating multiple-choice question-answering as an entailment problem. SCITAIL is the first entailment set that is created solely from natural sentences that already exist independently "in the wild" rather than sentences…  (More)
    • AAAI 2018
      Daniel Khashabi, Tushar Khot, Ashish Sabharwal, and Dan Roth
      We propose a novel method for exploiting the semantic structure of text to answer multiple-choice questions. The approach is especially suitable for domains that require reasoning over a diverse set of linguistic constructs but have limited training data. To address these challenges, we present the…  (More)
    • AAAI 2018
      Jonathan Kuck, Ashish Sabharwal, and Stefano Ermon
      Rademacher complexity is often used to characterize the learnability of a hypothesis class and is known to be related to the class size. We leverage this observation and introduce a new technique for estimating the size of an arbitrary weighted set, defined as the sum of weights of all elements in…  (More)
    • AAAI 2018
      Yonatan Bisk, Kevin J. Shih, Yejin Choi, and Daniel Marcu
      In this paper, we study the problem of mapping natural language instructions to complex spatial actions in a 3D blocks world. We first introduce a new dataset that pairs complex 3D spatial operations to rich natural language descriptions that require complex spatial and pragmatic interpretations…  (More)