mtybilly/Instruct2DS huggingface dataset:
Large-scale Exploration of Neural Relation Classification Architectures
Authors: Hoang-Quynh Le, Duy-Cat Can, Sinh T. Vu, Thanh Hai Dang, Mohammad Taher Pilehvar, Nigel Collier
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Experimental performance on the task of relation classification has generally improved using deep neural network architectures. One major drawback of reported studies is that individual models have been evaluated on a very narrow range of datasets, raising questions about the adaptability of the architectures, while making comparisons between approaches difficult. In this work, we present a systematic large-scale analysis of neural relation classification architectures on six benchmark datasets with widely varying characteristics. We propose a novel multi-channel LSTM model combined with a CNN that takes advantage of all currently popular linguistic and architectural features. Our \textquoteleftMan for All Seasons' approach achieves state-of-the-art performance on two datasets. More importantly, in our view, the model allowed us to obtain direct insights into the continued challenges faced by neural language models on this task.
Possessors Change Over Time: A Case Study with Artworks
Authors: Dhivya Chinnappa, Eduardo Blanco
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: This paper presents a corpus and experimental results to extract possession relations over time. We work with Wikipedia articles about artworks, and extract possession relations along with temporal information indicating when these relations are true. The annotation scheme yields many possessors over time for a given artwork, and experimental results show that an LSTM ensemble can automate the task.
Using Lexical Alignment and Referring Ability to Address Data Sparsity in Situated Dialog Reference Resolution
Authors: Todd Shore, Gabriel Skantze
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Referring to entities in situated dialog is a collaborative process, whereby interlocutors often expand, repair and/or replace referring expressions in an iterative process, converging on conceptual pacts of referring language use in doing so. Nevertheless, much work on exophoric reference resolution (i.e. resolution of references to entities outside of a given text) follows a literary model, whereby individual referring expressions are interpreted as unique identifiers of their referents given the state of the dialog the referring expression is initiated. In this paper, we address this collaborative nature to improve dialogic reference resolution in two ways: First, we trained a words-as-classifiers logistic regression model of word semantics and incrementally adapt the model to idiosyncratic language between dyad partners during evaluation of the dialog. We then used these semantic models to learn the general referring ability of each word, which is independent of referent features. These methods facilitate accurate automatic reference resolution in situated dialog without annotation of referring expressions, even with little background data.
Subgoal Discovery for Hierarchical Dialogue Policy Learning
Authors: Da Tang, Xiujun Li, Jianfeng Gao, Chong Wang, Lihong Li, Tony Jebara
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Developing agents to engage in complex goal-oriented dialogues is challenging partly because the main learning signals are very sparse in long conversations. In this paper, we propose a divide-and-conquer approach that discovers and exploits the hidden structure of the task to enable efficient policy learning. First, given successful example dialogues, we propose the Subgoal Discovery Network (SDN) to divide a complex goal-oriented task into a set of simpler subgoals in an unsupervised fashion. We then use these subgoals to learn a multi-level policy by hierarchical reinforcement learning. We demonstrate our method by building a dialogue agent for the composite task of travel planning. Experiments with simulated and real users show that our approach performs competitively against a state-of-the-art method that requires human-defined subgoals. Moreover, we show that the learned subgoals are often human comprehensible.
Supervised Clustering of Questions into Intents for Dialog System Applications
Authors: Iryna Haponchyk, Antonio Uva, Seunghak Yu, Olga Uryupina, Alessandro Moschitti
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Modern automated dialog systems require complex dialog managers able to deal with user intent triggered by high-level semantic questions. In this paper, we propose a model for automatically clustering questions into user intents to help the design tasks. Since questions are short texts, uncovering their semantics to group them together can be very challenging. We approach the problem by using powerful semantic classifiers from question duplicate/matching research along with a novel idea of supervised clustering methods based on structured output. We test our approach on two intent clustering corpora, showing an impressive improvement over previous methods for two languages/domains.
Towards Exploiting Background Knowledge for Building Conversation Systems
Authors: Nikita Moghe, Siddhartha Arora, Suman Banerjee, Mitesh M. Khapra
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Existing dialog datasets contain a sequence of utterances and responses without any explicit background knowledge associated with them. This has resulted in the development of models which treat conversation as a sequence-to-sequence generation task (i.e., given a sequence of utterances generate the response sequence). This is not only an overly simplistic view of conversation but it is also emphatically different from the way humans converse by heavily relying on their background knowledge about the topic (as opposed to simply relying on the previous sequence of utterances). For example, it is common for humans to (involuntarily) produce utterances which are copied or suitably modified from background articles they have read about the topic. To facilitate the development of such natural conversation models which mimic the human process of conversing, we create a new dataset containing movie chats wherein each response is explicitly generated by copying and/or modifying sentences from unstructured background knowledge such as plots, comments and reviews about the movie. We establish baseline results on this dataset (90K utterances from 9K conversations) using three different models: (i) pure generation based models which ignore the background knowledge (ii) generation based models which learn to copy information from the background knowledge when required and (iii) span prediction based models which predict the appropriate response span in the background knowledge.
Decoupling Strategy and Generation in Negotiation Dialogues
Authors: He He, Derek Chen, Anusha Balakrishnan, Percy Liang
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We consider negotiation settings in which two agents use natural language to bargain on goods. Agents need to decide on both high-level strategy (e.g., proposing \$50) and the execution of that strategy (e.g., generating “The bike is brand new. Selling for just \$50!”). Recent work on negotiation trains neural models, but their end-to-end nature makes it hard to control their strategy, and reinforcement learning tends to lead to degenerate solutions. In this paper, we propose a modular approach based on coarse dialogue acts (e.g., propose(price=50)) that decouples strategy and generation. We show that we can flexibly set the strategy using supervised learning, reinforcement learning, or domain-specific knowledge without degeneracy, while our retrieval-based generation can maintain context-awareness and produce diverse utterances. We test our approach on the recently proposed DEALORNODEAL game, and we also collect a richer dataset based on real items on Craigslist. Human evaluation shows that our systems achieve higher task success rate and more human-like negotiation behavior than previous approaches.
Large-scale Cloze Test Dataset Created by Teachers
Authors: Qizhe Xie, Guokun Lai, Zihang Dai, Eduard Hovy
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Cloze tests are widely adopted in language exams to evaluate students' language proficiency. In this paper, we propose the first large-scale human-created cloze test dataset CLOTH, containing questions used in middle-school and high-school language exams. With missing blanks carefully created by teachers and candidate choices purposely designed to be nuanced, CLOTH requires a deeper language understanding and a wider attention span than previously automatically-generated cloze datasets. We test the performance of dedicatedly designed baseline models including a language model trained on the One Billion Word Corpus and show humans outperform them by a significant margin. We investigate the source of the performance gap, trace model deficiencies to some distinct properties of CLOTH, and identify the limited ability of comprehending the long-term context to be the key bottleneck.
emrQA: A Large Corpus for Question Answering on Electronic Medical Records
Authors: Anusri Pampari, Preethi Raghavan, Jennifer Liang, Jian Peng
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We propose a novel methodology to generate domain-specific large-scale question answering (QA) datasets by re-purposing existing annotations for other NLP tasks. We demonstrate an instance of this methodology in generating a large-scale QA dataset for electronic medical records by leveraging existing expert annotations on clinical notes for various NLP tasks from the community shared i2b2 datasets. The resulting corpus (emrQA) has 1 million questions-logical form and 400,000+ question-answer evidence pairs. We characterize the dataset and explore its learning potential by training baseline models for question to logical form and question to answer mapping.
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Authors: Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, Christopher D. Manning
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems' ability to extract relevant facts and perform necessary comparison. We show that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions.
Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering
Authors: Todor Mihaylov, Peter Clark, Tushar Khot, Ashish Sabharwal
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: 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 1326 elementary level science facts. Roughly 6000 questions probe an understanding of these facts and their application to novel situations. This requires combining an open book fact (e.g., metals conduct electricity) with broad common knowledge (e.g., a suit of armor is made of metal) obtained from other sources. While existing QA datasets over documents or knowledge bases, being generally self-contained, focus on linguistic understanding, OpenBookQA probes a deeper understanding of both the topic---in the context of common knowledge---and the language it is expressed in. Human performance on OpenBookQA is close to 92%, but many state-of-the-art pre-trained QA methods perform surprisingly poorly, worse than several simple neural baselines we develop. Our oracle experiments designed to circumvent the knowledge retrieval bottleneck demonstrate the value of both the open book and additional facts. We leave it as a challenge to solve the retrieval problem in this multi-hop setting and to close the large gap to human performance.
Evaluating Theory of Mind in Question Answering
Authors: Aida Nematzadeh, Kaylee Burns, Erin Grant, Alison Gopnik, Tom Griffiths
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We propose a new dataset for evaluating question answering models with respect to their capacity to reason about beliefs. Our tasks are inspired by theory-of-mind experiments that examine whether children are able to reason about the beliefs of others, in particular when those beliefs differ from reality. We evaluate a number of recent neural models with memory augmentation. We find that all fail on our tasks, which require keeping track of inconsistent states of the world; moreover, the models' accuracy decreases notably when random sentences are introduced to the tasks at test.
A Unified Syntax-aware Framework for Semantic Role Labeling
Authors: Zuchao Li, Shexia He, Jiaxun Cai, Zhuosheng Zhang, Hai Zhao, Gongshen Liu, Linlin Li, Luo Si
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Semantic role labeling (SRL) aims to recognize the predicate-argument structure of a sentence. Syntactic information has been paid a great attention over the role of enhancing SRL. However, the latest advance shows that syntax would not be so important for SRL with the emerging much smaller gap between syntax-aware and syntax-agnostic SRL. To comprehensively explore the role of syntax for SRL task, we extend existing models and propose a unified framework to investigate more effective and more diverse ways of incorporating syntax into sequential neural networks. Exploring the effect of syntactic input quality on SRL performance, we confirm that high-quality syntactic parse could still effectively enhance syntactically-driven SRL. Using empirically optimized integration strategy, we even enlarge the gap between syntax-aware and syntax-agnostic SRL. Our framework achieves state-of-the-art results on CoNLL-2009 benchmarks both for English and Chinese, substantially outperforming all previous models.
Semantics as a Foreign Language
Authors: Gabriel Stanovsky, Ido Dagan
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We propose a novel approach to semantic dependency parsing (SDP) by casting the task as an instance of multi-lingual machine translation, where each semantic representation is a different foreign dialect. To that end, we first generalize syntactic linearization techniques to account for the richer semantic dependency graph structure. Following, we design a neural sequence-to-sequence framework which can effectively recover our graph linearizations, performing almost on-par with previous SDP state-of-the-art while requiring less parallel training annotations. Beyond SDP, our linearization technique opens the door to integration of graph-based semantic representations as features in neural models for downstream applications.
An AMR Aligner Tuned by Transition-based Parser
Authors: Yijia Liu, Wanxiang Che, Bo Zheng, Bing Qin, Ting Liu
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: In this paper, we propose a new rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser. Our aligner is further tuned by our oracle parser via picking the alignment that leads to the highest-scored achievable AMR graph. Experimental results show that our aligner outperforms the rule-based aligner in previous work by achieving higher alignment F1 score and consistently improving two open-sourced AMR parsers. Based on our aligner and transition system, we develop a transition-based AMR parser that parses a sentence into its AMR graph directly. An ensemble of our parsers with only words and POS tags as input leads to 68.4 Smatch F1 score, which outperforms the current state-of-the-art parser.
Dependency-based Hybrid Trees for Semantic Parsing
Authors: Zhanming Jie, Wei Lu
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We propose a novel dependency-based hybrid tree model for semantic parsing, which converts natural language utterance into machine interpretable meaning representations. Unlike previous state-of-the-art models, the semantic information is interpreted as the latent dependency between the natural language words in our joint representation. Such dependency information can capture the interactions between the semantics and natural language words. We integrate a neural component into our model and propose an efficient dynamic-programming algorithm to perform tractable inference. Through extensive experiments on the standard multilingual GeoQuery dataset with eight languages, we demonstrate that our proposed approach is able to achieve state-of-the-art performance across several languages. Analysis also justifies the effectiveness of using our new dependency-based representation.
Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations
Authors: Dipendra Misra, Ming-Wei Chang, Xiaodong He, Wen-tau Yih
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: 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, we bias the search procedure towards semantic parses that are more compatible to the text, which provide better supervision signals for training. In addition, we propose an update equation that generalizes three different families of learning algorithms, which enables fast model exploration. When experimented on a recently proposed sequential question answering dataset, our framework leads to a new state-of-the-art model that outperforms previous work by 5.0% absolute on exact match accuracy.
Sentence Compression for Arbitrary Languages via Multilingual Pivoting
Authors: Jonathan Mallinson, Rico Sennrich, Mirella Lapata
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: In this paper we advocate the use of bilingual corpora which are abundantly available for training sentence compression models. Our approach borrows much of its machinery from neural machine translation and leverages bilingual pivoting: compressions are obtained by translating a source string into a foreign language and then back-translating it into the source while controlling the translation length. Our model can be trained for any language as long as a bilingual corpus is available and performs arbitrary rewrites without access to compression specific data. We release. Moss, a new parallel Multilingual Compression dataset for English, German, and French which can be used to evaluate compression models across languages and genres.
Unsupervised Cross-lingual Transfer of Word Embedding Spaces
Authors: Ruochen Xu, Yiming Yang, Naoki Otani, Yuexin Wu
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Cross-lingual transfer of word embeddings aims to establish the semantic mappings among words in different languages by learning the transformation functions over the corresponding word embedding spaces. Successfully solving this problem would benefit many downstream tasks such as to translate text classification models from resource-rich languages (e.g. English) to low-resource languages. Supervised methods for this problem rely on the availability of cross-lingual supervision, either using parallel corpora or bilingual lexicons as the labeled data for training, which may not be available for many low resource languages. This paper proposes an unsupervised learning approach that does not require any cross-lingual labeled data. Given two monolingual word embedding spaces for any language pair, our algorithm optimizes the transformation functions in both directions simultaneously based on distributional matching as well as minimizing the back-translation losses. We use a neural network implementation to calculate the Sinkhorn distance, a well-defined distributional similarity measure, and optimize our objective through back-propagation. Our evaluation on benchmark datasets for bilingual lexicon induction and cross-lingual word similarity prediction shows stronger or competitive performance of the proposed method compared to other state-of-the-art supervised and unsupervised baseline methods over many language pairs.
XNLI: Evaluating Cross-lingual Sentence Representations
Authors: Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel Bowman, Holger Schwenk, Veselin Stoyanov
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a single language (usually English), and cannot be directly used beyond that language. Since collecting data in every language is not realistic, there has been a growing interest in cross-lingual language understanding (XLU) and low-resource cross-language transfer. In this work, we construct an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 14 languages, including low-resource languages such as Swahili and Urdu. We hope that our dataset, dubbed XNLI, will catalyze research in cross-lingual sentence understanding by providing an informative standard evaluation task. In addition, we provide several baselines for multilingual sentence understanding, including two based on machine translation systems, and two that use parallel data to train aligned multilingual bag-of-words and LSTM encoders. We find that XNLI represents a practical and challenging evaluation suite, and that directly translating the test data yields the best performance among available baselines.
Joint Multilingual Supervision for Cross-lingual Entity Linking
Authors: Shyam Upadhyay, Nitish Gupta, Dan Roth
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Cross-lingual Entity Linking (XEL) aims to ground entity mentions written in any language to an English Knowledge Base (KB), such as Wikipedia. XEL for most languages is challenging, owing to limited availability of resources as supervision. We address this challenge by developing the first XEL approach that combines supervision from multiple languages jointly. This enables our approach to: (a) augment the limited supervision in the target language with additional supervision from a high-resource language (like English), and (b) train a single entity linking model for multiple languages, improving upon individually trained models for each language. Extensive evaluation on three benchmark datasets across 8 languages shows that our approach significantly improves over the current state-of-the-art. We also provide analyses in two limited resource settings: (a) zero-shot setting, when no supervision in the target language is available, and in (b) low-resource setting, when some supervision in the target language is available. Our analysis provides insights into the limitations of zero-shot XEL approaches in realistic scenarios, and shows the value of joint supervision in low-resource settings.
Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition
Authors: Tao Ge, Qing Dou, Heng Ji, Lei Cui, Baobao Chang, Zhifang Sui, Furu Wei, Ming Zhou
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: This paper proposes to study fine-grained coordinated cross-lingual text stream alignment through a novel information network decipherment paradigm. We use Burst Information Networks as media to represent text streams and present a simple yet effective network decipherment algorithm with diverse clues to decipher the networks for accurate text stream alignment. Experiments on Chinese-English news streams show our approach not only outperforms previous approaches on bilingual lexicon extraction from coordinated text streams but also can harvest high-quality alignments from large amounts of streaming data for endless language knowledge mining, which makes it promising to be a new paradigm for automatic language knowledge acquisition.
WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition
Authors: Yufeng Diao, Hongfei Lin, Di Wu, Liang Yang, Kan Xu, Zhihao Yang, Jian Wang, Shaowu Zhang, Bo Xu, Dongyu Zhang
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Homographic puns have a long history in human writing, widely used in written and spoken literature, which usually occur in a certain syntactic or stylistic structure. How to recognize homographic puns is an important research. However, homographic pun recognition does not solve very well in existing work. In this work, we first use WordNet to understand and expand word embedding for settling the polysemy of homographic puns, and then propose a WordNet-Encoded Collocation-Attention network model (WECA) which combined with the context weights for recognizing the puns. Our experiments on the SemEval2017 Task7 and Pun of the Day demonstrate that the proposed model is able to distinguish between homographic pun and non-homographic pun texts. We show the effectiveness of the model to present the capability of choosing qualitatively informative words. The results show that our model achieves the state-of-the-art performance on homographic puns recognition.
A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check
Authors: Dingmin Wang, Yan Song, Jing Li, Jialong Han, Haisong Zhang
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Chinese spelling check (CSC) is a challenging yet meaningful task, which not only serves as a preprocessing in many natural language processing(NLP) applications, but also facilitates reading and understanding of running texts in peoples' daily lives. However, to utilize data-driven approaches for CSC, there is one major limitation that annotated corpora are not enough in applying algorithms and building models. In this paper, we propose a novel approach of constructing CSC corpus with automatically generated spelling errors, which are either visually or phonologically resembled characters, corresponding to the OCR- and ASR-based methods, respectively. Upon the constructed corpus, different models are trained and evaluated for CSC with respect to three standard test sets. Experimental results demonstrate the effectiveness of the corpus, therefore confirm the validity of our approach.
Neural Quality Estimation of Grammatical Error Correction
Authors: Shamil Chollampatt, Hwee Tou Ng
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Grammatical error correction (GEC) systems deployed in language learning environments are expected to accurately correct errors in learners' writing. However, in practice, they often produce spurious corrections and fail to correct many errors, thereby misleading learners. This necessitates the estimation of the quality of output sentences produced by GEC systems so that instructors can selectively intervene and re-correct the sentences which are poorly corrected by the system and ensure that learners get accurate feedback. We propose the first neural approach to automatic quality estimation of GEC output sentences that does not employ any hand-crafted features. Our system is trained in a supervised manner on learner sentences and corresponding GEC system outputs with quality score labels computed using human-annotated references. Our neural quality estimation models for GEC show significant improvements over a strong feature-based baseline. We also show that a state-of-the-art GEC system can be improved when quality scores are used as features for re-ranking the N-best candidates.
Transferring from Formal Newswire Domain with Hypernet for Twitter POS Tagging
Authors: Tao Gui, Qi Zhang, Jingjing Gong, Minlong Peng, Di Liang, Keyu Ding, Xuanjing Huang
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Part-of-Speech (POS) tagging for Twitter has received considerable attention in recent years. Because most POS tagging methods are based on supervised models, they usually require a large amount of labeled data for training. However, the existing labeled datasets for Twitter are much smaller than those for newswire text. Hence, to help POS tagging for Twitter, most domain adaptation methods try to leverage newswire datasets by learning the shared features between the two domains. However, from a linguistic perspective, Twitter users not only tend to mimic the formal expressions of traditional media, like news, but they also appear to be developing linguistically informal styles. Therefore, POS tagging for the formal Twitter context can be learned together with the newswire dataset, while POS tagging for the informal Twitter context should be learned separately. To achieve this task, in this work, we propose a hypernetwork-based method to generate different parameters to separately model contexts with different expression styles. Experimental results on three different datasets show that our approach achieves better performance than state-of-the-art methods in most cases.
Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit
Authors: Amrith Krishna, Bishal Santra, Sasi Prasanth Bandaru, Gaurav Sahu, Vishnu Dutt Sharma, Pavankumar Satuluri, Pawan Goyal
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: The configurational information in sentences of a free word order language such as Sanskrit is of limited use. Thus, the context of the entire sentence will be desirable even for basic processing tasks such as word segmentation. We propose a structured prediction framework that jointly solves the word segmentation and morphological tagging tasks in Sanskrit. We build an energy based model where we adopt approaches generally employed in graph based parsing techniques (McDonald et al., 2005a; Carreras, 2007). Our model outperforms the state of the art with an F-Score of 96.92 (percentage improvement of 7.06%) while using less than one tenth of the task-specific training data. We find that the use of a graph based approach instead of a traditional lattice-based sequential labelling approach leads to a percentage gain of 12.6% in F-Score for the segmentation task.
A Challenge Set and Methods for Noun-Verb Ambiguity
Authors: Ali Elkahky, Kellie Webster, Daniel Andor, Emily Pitler
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: English part-of-speech taggers regularly make egregious errors related to noun-verb ambiguity, despite having achieved 97%+ accuracy on the WSJ Penn Treebank since 2002. These mistakes have been difficult to quantify and make taggers less useful to downstream tasks such as translation and text-to-speech synthesis. This paper creates a new dataset of over 30,000 naturally-occurring non-trivial examples of noun-verb ambiguity. Taggers within 1% of each other when measured on the WSJ have accuracies ranging from 57% to 75% accuracy on this challenge set. Enhancing the strongest existing tagger with contextual word embeddings and targeted training data improves its accuracy to 89%, a 14% absolute (52% relative) improvement. Downstream, using just this enhanced tagger yields a 28% reduction in error over the prior best learned model for homograph disambiguation for textto-speech synthesis.
What do character-level models learn about morphology? The case of dependency parsing
Authors: Clara Vania, Andreas Grivas, Adam Lopez
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: When parsing morphologically-rich languages with neural models, it is beneficial to model input at the character level, and it has been claimed that this is because character-level models learn morphology. We test these claims by comparing character-level models to an oracle with access to explicit morphological analysis on twelve languages with varying morphological typologies. Our results highlight many strengths of character-level models, but also show that they are poor at disambiguating some words, particularly in the face of case syncretism. We then demonstrate that explicitly modeling morphological case improves our best model, showing that character-level models can benefit from targeted forms of explicit morphological modeling.
Learning Better Internal Structure of Words for Sequence Labeling
Authors: Yingwei Xin, Ethan Hart, Vibhuti Mahajan, Jean-David Ruvini
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Character-based neural models have recently proven very useful for many NLP tasks. However, there is a gap of sophistication between methods for learning representations of sentences and words. While, most character models for learning representations of sentences are deep and complex, models for learning representations of words are shallow and simple. Also, in spite of considerable research on learning character embeddings, it is still not clear which kind of architecture is the best for capturing character-to-word representations. To address these questions, we first investigate the gaps between methods for learning word and sentence representations. We conduct detailed experiments and comparisons on different state-of-the-art convolutional models, and also investigate the advantages and disadvantages of their constituents. Furthermore, we propose IntNet, a funnel-shaped wide convolutional neural architecture with no down-sampling for learning representations of the internal structure of words by composing their characters from limited, supervised training corpora. We evaluate our proposed model on six sequence labeling datasets, including named entity recognition, part-of-speech tagging, and syntactic chunking. Our in-depth analysis shows that IntNet significantly outperforms other character embedding models and obtains new state-of-the-art performance without relying on any external knowledge or resources.
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection
Authors: Devamanyu Hazarika, Soujanya Poria, Rada Mihalcea, Erik Cambria, Roger Zimmermann
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Emotion recognition in conversations is crucial for building empathetic machines. Present works in this domain do not explicitly consider the inter-personal influences that thrive in the emotional dynamics of dialogues. To this end, we propose Interactive COnversational memory Network (ICON), a multimodal emotion detection framework that extracts multimodal features from conversational videos and hierarchically models the self- and inter-speaker emotional influences into global memories. Such memories generate contextual summaries which aid in predicting the emotional orientation of utterance-videos. Our model outperforms state-of-the-art networks on multiple classification and regression tasks in two benchmark datasets.
Discriminative Learning of Open-Vocabulary Object Retrieval and Localization by Negative Phrase Augmentation
Authors: Ryota Hinami, Shin′ichi Satoh
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Thanks to the success of object detection technology, we can retrieve objects of the specified classes even from huge image collections. However, the current state-of-the-art object detectors (such as Faster R-CNN) can only handle pre-specified classes. In addition, large amounts of positive and negative visual samples are required for training. In this paper, we address the problem of open-vocabulary object retrieval and localization, where the target object is specified by a textual query (e.g., a word or phrase). We first propose Query-Adaptive R-CNN, a simple extension of Faster R-CNN adapted to open-vocabulary queries, by transforming the text embedding vector into an object classifier and localization regressor. Then, for discriminative training, we then propose negative phrase augmentation (NPA) to mine hard negative samples which are visually similar to the query and at the same time semantically mutually exclusive of the query. The proposed method can retrieve and localize objects specified by a textual query from one million images in only 0.5 seconds with high precision.
Grounding Semantic Roles in Images
Authors: Carina Silberer, Manfred Pinkal
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We address the task of visual semantic role labeling (vSRL), the identification of the participants of a situation or event in a visual scene, and their labeling with their semantic relations to the event or situation. We render candidate participants as image regions of objects, and train a model which learns to ground roles in the regions which depict the corresponding participant. Experimental results demonstrate that we can train a vSRL model without reliance on prohibitive image-based role annotations, by utilizing noisy data which we extract automatically from image captions using a linguistic SRL system. Furthermore, our model induces frame---semantic visual representations, and their comparison to previous work on supervised visual verb sense disambiguation yields overall better results.
Commonsense Justification for Action Explanation
Authors: Shaohua Yang, Qiaozi Gao, Sari Sadiya, Joyce Chai
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: To enable collaboration and communication between humans and agents, this paper investigates learning to acquire commonsense evidence for action justification. In particular, we have developed an approach based on the generative Conditional Variational Autoencoder(CVAE) that models object relations/attributes of the world as latent variables and jointly learns a performer that predicts actions and an explainer that gathers commonsense evidence to justify the action. Our empirical results have shown that, compared to a typical attention-based model, CVAE achieves significantly higher performance in both action prediction and justification. A human subject study further shows that the commonsense evidence gathered by CVAE can be communicated to humans to achieve a significantly higher common ground between humans and agents.
Learning Personas from Dialogue with Attentive Memory Networks
Authors: Eric Chu, Prashanth Vijayaraghavan, Deb Roy
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: The ability to infer persona from dialogue can have applications in areas ranging from computational narrative analysis to personalized dialogue generation. We introduce neural models to learn persona embeddings in a supervised character trope classification task. The models encode dialogue snippets from IMDB into representations that can capture the various categories of film characters. The best-performing models use a multi-level attention mechanism over a set of utterances. We also utilize prior knowledge in the form of textual descriptions of the different tropes. We apply the learned embeddings to find similar characters across different movies, and cluster movies according to the distribution of the embeddings. The use of short conversational text as input, and the ability to learn from prior knowledge using memory, suggests these methods could be applied to other domains.
Grounding language acquisition by training semantic parsers using captioned videos
Authors: Candace Ross, Andrei Barbu, Yevgeni Berzak, Battushig Myanganbayar, Boris Katz
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We develop a semantic parser that is trained in a grounded setting using pairs of videos captioned with sentences. This setting is both data-efficient, requiring little annotation, and similar to the experience of children where they observe their environment and listen to speakers. The semantic parser recovers the meaning of English sentences despite not having access to any annotated sentences. It does so despite the ambiguity inherent in vision where a sentence may refer to any combination of objects, object properties, relations or actions taken by any agent in a video. For this task, we collected a new dataset for grounded language acquisition. Learning a grounded semantic parser --- turning sentences into logical forms using captioned videos --- can significantly expand the range of data that parsers can be trained on, lower the effort of training a semantic parser, and ultimately lead to a better understanding of child language acquisition.
Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation
Authors: Xiaoxue Zang, Ashwini Pokle, Marynel Vázquez, Kevin Chen, Juan Carlos Niebles, Alvaro Soto, Silvio Savarese
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for behavioral robot navigation. We use attention models to connect information from both the user instructions and a topological representation of the environment. We evaluate our model`s performance on a new dataset containing 10,050 pairs of navigation instructions. Our model significantly outperforms baseline approaches. Furthermore, our results suggest that it is possible to leverage the environment map as a relevant knowledge base to facilitate the translation of free-form navigational instruction.
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
Authors: Dipendra Misra, Andrew Bennett, Valts Blukis, Eyvind Niklasson, Max Shatkhin, Yoav Artzi
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We propose to decompose instruction execution to goal prediction and action generation. We design a model that maps raw visual observations to goals using LINGUNET, a language-conditioned image generation network, and then generates the actions required to complete them. Our model is trained from demonstration only without external resources. To evaluate our approach, we introduce two benchmarks for instruction following: LANI, a navigation task; and CHAI, where an agent executes household instructions. Our evaluation demonstrates the advantages of our model decomposition, and illustrates the challenges posed by our new benchmarks.
Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse Effects
Authors: Cory Shain, William Schuler
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Researchers in computational psycholinguistics frequently use linear models to study time series data generated by human subjects. However, time series may violate the assumptions of these models through temporal diffusion, where stimulus presentation has a lingering influence on the response as the rest of the experiment unfolds. This paper proposes a new statistical model that borrows from digital signal processing by recasting the predictors and response as convolutionally-related signals, using recent advances in machine learning to fit latent impulse response functions (IRFs) of arbitrary shape. A synthetic experiment shows successful recovery of true latent IRFs, and psycholinguistic experiments reveal plausible, replicable, and fine-grained estimates of latent temporal dynamics, with comparable or improved prediction quality to widely-used alternatives.
Is this Sentence Difficult? Do you Agree?
Authors: Dominique Brunato, Lorenzo De Mattei, Felice Dell′Orletta, Benedetta Iavarone, Giulia Venturi
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: In this paper, we present a crowdsourcing-based approach to model the human perception of sentence complexity. We collect a large corpus of sentences rated with judgments of complexity for two typologically-different languages, Italian and English. We test our approach in two experimental scenarios aimed to investigate the contribution of a wide set of lexical, morpho-syntactic and syntactic phenomena in predicting i) the degree of agreement among annotators independently from the assigned judgment and ii) the perception of sentence complexity.