pwc-archive/papers-with-abstracts huggingface dataset:
Learning Prototypical Goal Activities for Locations
Authors: Tianyu Jiang, Ellen Riloff
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7

People go to different places to engage in activities that reflect their goals. For example, people go to restaurants to eat, libraries to study, and churches to pray. We refer to an activity that represents a common reason why people typically go to a location as a prototypical goal activity (goal-act). Our research aims to learn goal-acts for specific locations using a text corpus and semi-supervised learning. First, we extract activities and locations that co-occur in goal-oriented syntactic patterns. Next, we create an activity profile matrix and apply a semi-supervised label propagation algorithm to iteratively revise the activity strengths for different locations using a small set of labeled data. We show that this approach outperforms several baseline methods when judged against goal-acts identified by human annotators.
Guess Me if You Can: Acronym Disambiguation for Enterprises
Authors: Yang Li, Bo Zhao, Ariel Fuxman, Fangbo Tao
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Question Answering

Acronyms are abbreviations formed from the initial components of words or phrases. In enterprises, people often use acronyms to make communications more efficient. However, acronyms could be difficult to understand for people who are not familiar with the subject matter (new employees, etc.), thereby affecting productivity. To alleviate such troubles, we study how to automatically resolve the true meanings of acronyms in a given context. Acronym disambiguation for enterprises is challenging for several reasons. First, acronyms may be highly ambiguous since an acronym used in the enterprise could have multiple internal and external meanings. Second, there are usually no comprehensive knowledge bases such as Wikipedia available in enterprises. Finally, the system should be generic to work for any enterprise. In this work we propose an end-to-end framework to tackle all these challenges. The framework takes the enterprise corpus as input and produces a high-quality acronym disambiguation system as output. Our disambiguation models are trained via distant supervised learning, without requiring any manually labeled training examples. Therefore, our proposed framework can be deployed to any enterprise to support high-quality acronym disambiguation. Experimental results on real world data justified the effectiveness of our system.
Exemplar Encoder-Decoder for Neural Conversation Generation
Authors: P, Gaurav ey, Danish Contractor, Vineet Kumar, Sachindra Joshi
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Decoder, Retrieval

In this paper we present the Exemplar Encoder-Decoder network (EED), a novel conversation model that learns to utilize \textit{similar} examples from training data to generate responses. Similar conversation examples (context-response pairs) from training data are retrieved using a traditional TF-IDF based retrieval model and the corresponding responses are used by our decoder to generate the ground truth response. The contribution of each retrieved response is weighed by the similarity of corresponding context with the input context. As a result, our model learns to assign higher similarity scores to those retrieved contexts whose responses are crucial for generating the final response. We present detailed experiments on two large data sets and we find that our method out-performs state of the art sequence to sequence generative models on several recently proposed evaluation metrics.
DialSQL: Dialogue Based Structured Query Generation
Authors: Izzeddin Gur, Semih Yavuz, Yu Su, Xifeng Yan
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Semantic Parsing, Translation

The recent advance in deep learning and semantic parsing has significantly improved the translation accuracy of natural language questions to structured queries. However, further improvement of the existing approaches turns out to be quite challenging. Rather than solely relying on algorithmic innovations, in this work, we introduce DialSQL, a dialogue-based structured query generation framework that leverages human intelligence to boost the performance of existing algorithms via user interaction. DialSQL is capable of identifying potential errors in a generated SQL query and asking users for validation via simple multi-choice questions. User feedback is then leveraged to revise the query. We design a generic simulator to bootstrap synthetic training dialogues and evaluate the performance of DialSQL on the WikiSQL dataset. Using SQLNet as a black box query generation tool, DialSQL improves its performance from 61.3{\%} to 69.0{\%} using only 2.4 validation questions per dialogue.
Are BLEU and Meaning Representation in Opposition?
Authors: Ond{\v{r}}ej C{\'\i}fka, Ond{\v{r}}ej Bojar
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: General Classification, Machine Translation, NMT, Semantic Textual Similarity, Sentence, Translation

One of possible ways of obtaining continuous-space sentence representations is by training neural machine translation (NMT) systems. The recent attention mechanism however removes the single point in the neural network from which the source sentence representation can be extracted. We propose several variations of the attentive NMT architecture bringing this meeting point back. Empirical evaluation suggests that the better the translation quality, the worse the learned sentence representations serve in a wide range of classification and similarity tasks.
The Hitchhiker's Guide to Testing Statistical Significance in Natural Language Processing
Authors: Rotem Dror, Gili Baumer, Segev Shlomov, Roi Reichart
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Survey

Statistical significance testing is a standard statistical tool designed to ensure that experimental results are not coincidental. In this opinion/ theoretical paper we discuss the role of statistical significance testing in Natural Language Processing (NLP) research. We establish the fundamental concepts of significance testing and discuss the specific aspects of NLP tasks, experimental setups and evaluation measures that affect the choice of significance tests in NLP research. Based on this discussion we propose a simple practical protocol for statistical significance test selection in NLP setups and accompany this protocol with a brief survey of the most relevant tests. We then survey recent empirical papers published in ACL and TACL during 2017 and show that while our community assigns great value to experimental results, statistical significance testing is often ignored or misused. We conclude with a brief discussion of open issues that should be properly addressed so that this important tool can be applied. in NLP research in a statistically sound manner.
Twitter Universal Dependency Parsing for African-American and Mainstream American English
Authors: Su Lin Blodgett, Johnny Wei, Brendan O{'}Connor
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Dependency Parsing, Information Retrieval, Language Identification, Part-Of-Speech Tagging, Sentiment Analysis

Due to the presence of both Twitter-specific conventions and non-standard and dialectal language, Twitter presents a significant parsing challenge to current dependency parsing tools. We broaden English dependency parsing to handle social media English, particularly social media African-American English (AAE), by developing and annotating a new dataset of 500 tweets, 250 of which are in AAE, within the Universal Dependencies 2.0 framework. We describe our standards for handling Twitter- and AAE-specific features and evaluate a variety of cross-domain strategies for improving parsing with no, or very little, in-domain labeled data, including a new data synthesis approach. We analyze these methods{'} impact on performance disparities between AAE and Mainstream American English tweets, and assess parsing accuracy for specific AAE lexical and syntactic features. Our annotated data and a parsing model are available at: \url{http://slanglab.cs.umass.edu/TwitterAAE/}.
LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better
Authors: Adhiguna Kuncoro, Chris Dyer, John Hale, Dani Yogatama, Stephen Clark, Phil Blunsom
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Diagnostic, Language Modeling, Language Modelling, Machine Translation, Text Summarization

Language exhibits hierarchical structure, but recent work using a subject-verb agreement diagnostic argued that state-of-the-art language models, LSTMs, fail to learn long-range syntax sensitive dependencies. Using the same diagnostic, we show that, in fact, LSTMs do succeed in learning such dependencies{---}provided they have enough capacity. We then explore whether models that have access to explicit syntactic information learn agreement more effectively, and how the way in which this structural information is incorporated into the model impacts performance. We find that the mere presence of syntactic information does not improve accuracy, but when model architecture is determined by syntax, number agreement is improved. Further, we find that the choice of how syntactic structure is built affects how well number agreement is learned: top-down construction outperforms left-corner and bottom-up variants in capturing non-local structural dependencies.
Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures
Authors: Wenqiang Lei, Xisen Jin, Min-Yen Kan, Zhaochun Ren, Xiangnan He, Dawei Yin
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Task-Oriented Dialogue Systems

Existing solutions to task-oriented dialogue systems follow pipeline designs which introduces architectural complexity and fragility. We propose a novel, holistic, extendable framework based on a single sequence-to-sequence (seq2seq) model which can be optimized with supervised or reinforcement learning. A key contribution is that we design text spans named belief spans to track dialogue believes, allowing task-oriented dialogue systems to be modeled in a seq2seq way. Based on this, we propose a simplistic Two Stage CopyNet instantiation which emonstrates good scalability: significantly reducing model complexity in terms of number of parameters and training time by a magnitude. It significantly outperforms state-of-the-art pipeline-based methods on large datasets and retains a satisfactory entity match rate on out-of-vocabulary (OOV) cases where pipeline-designed competitors totally fail.
Global-Locally Self-Attentive Encoder for Dialogue State Tracking
Authors: Victor Zhong, Caiming Xiong, Richard Socher
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Automatic Speech Recognition (ASR), Dialogue State Tracking, Representation Learning, Speech Recognition, Spoken Language Understanding, Task-Oriented Dialogue Systems

Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems. In this paper, we propose the Global-Locally Self-Attentive Dialogue State Tracker (GLAD), which learns representations of the user utterance and previous system actions with global-local modules. Our model uses global modules to shares parameters between estimators for different types (called slots) of dialogue states, and uses local modules to learn slot-specific features. We show that this significantly improves tracking of rare states. GLAD obtains 88.3{\%} joint goal accuracy and 96.4{\%} request accuracy on the WoZ state tracking task, outperforming prior work by 3.9{\%} and 4.8{\%}. On the DSTC2 task, our model obtains 74.7{\%} joint goal accuracy and 97.3{\%} request accuracy, outperforming prior work by 1.3{\%} and 0.8{\%}
Tailored Sequence to Sequence Models to Different Conversation Scenarios
Authors: Hainan Zhang, Yanyan Lan, Jiafeng Guo, Jun Xu, Xue-Qi Cheng
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Chatbot, Dialogue Generation, Response Generation

Sequence to sequence (Seq2Seq) models have been widely used for response generation in the area of conversation. However, the requirements for different conversation scenarios are distinct. For example, customer service requires the generated responses to be specific and accurate, while chatbot prefers diverse responses so as to attract different users. The current Seq2Seq model fails to meet these diverse requirements, by using a general average likelihood as the optimization criteria. As a result, it usually generates safe and commonplace responses, such as {`}I don{'}t know{'}. In this paper, we propose two tailored optimization criteria for Seq2Seq to different conversation scenarios, i.e., the maximum generated likelihood for specific-requirement scenario, and the conditional value-at-risk for diverse-requirement scenario. Experimental results on the Ubuntu dialogue corpus (Ubuntu service scenario) and Chinese Weibo dataset (social chatbot scenario) show that our proposed models not only satisfies diverse requirements for different scenarios, but also yields better performances against traditional Seq2Seq models in terms of both metric-based and human evaluations.
Knowledge Diffusion for Neural Dialogue Generation
Authors: Shuman Liu, Hongshen Chen, Zhaochun Ren, Yang Feng, Qun Liu, Dawei Yin
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Dialogue Generation, Question Answering, Task-Oriented Dialogue Systems

End-to-end neural dialogue generation has shown promising results recently, but it does not employ knowledge to guide the generation and hence tends to generate short, general, and meaningless responses. In this paper, we propose a neural knowledge diffusion (NKD) model to introduce knowledge into dialogue generation. This method can not only match the relevant facts for the input utterance but diffuse them to similar entities. With the help of facts matching and entity diffusion, the neural dialogue generation is augmented with the ability of convergent and divergent thinking over the knowledge base. Our empirical study on a real-world dataset prove that our model is capable of generating meaningful, diverse and natural responses for both factoid-questions and knowledge grounded chi-chats. The experiment results also show that our model outperforms competitive baseline models significantly.
Generating Informative Responses with Controlled Sentence Function
Authors: Pei Ke, Jian Guan, Minlie Huang, Xiaoyan Zhu
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Position, Sentence, Text Generation, Vocal Bursts Type Prediction

Sentence function is a significant factor to achieve the purpose of the speaker, which, however, has not been touched in large-scale conversation generation so far. In this paper, we present a model to generate informative responses with controlled sentence function. Our model utilizes a continuous latent variable to capture various word patterns that realize the expected sentence function, and introduces a type controller to deal with the compatibility of controlling sentence function and generating informative content. Conditioned on the latent variable, the type controller determines the type (i.e., function-related, topic, and ordinary word) of a word to be generated at each decoding position. Experiments show that our model outperforms state-of-the-art baselines, and it has the ability to generate responses with both controlled sentence function and informative content.
Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP
Authors: Edoardo Maria Ponti, Roi Reichart, Anna Korhonen, Ivan Vuli{\'c}
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Cross-Lingual Transfer, Machine Translation, Representation Learning, Sentence, Sentence Similarity, Transfer Learning, Translation

The transfer or share of knowledge between languages is a potential solution to resource scarcity in NLP. However, the effectiveness of cross-lingual transfer can be challenged by variation in syntactic structures. Frameworks such as Universal Dependencies (UD) are designed to be cross-lingually consistent, but even in carefully designed resources trees representing equivalent sentences may not always overlap. In this paper, we measure cross-lingual syntactic variation, or anisomorphism, in the UD treebank collection, considering both morphological and structural properties. We show that reducing the level of anisomorphism yields consistent gains in cross-lingual transfer tasks. We introduce a source language selection procedure that facilitates effective cross-lingual parser transfer, and propose a typologically driven method for syntactic tree processing which reduces anisomorphism. Our results show the effectiveness of this method for both machine translation and cross-lingual sentence similarity, demonstrating the importance of syntactic structure compatibility for boosting cross-lingual transfer in NLP.
Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data
Authors: Adithya Pratapa, Gayatri Bhat, Monojit Choudhury, Sunayana Sitaram, D, S apat, ipan, Kalika Bali
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Automatic Speech Recognition (ASR), Language Identification, Language Modeling, Language Modelling, Sentiment Analysis, Speech Recognition, valid

Training language models for Code-mixed (CM) language is known to be a difficult problem because of lack of data compounded by the increased confusability due to the presence of more than one language. We present a computational technique for creation of grammatically valid artificial CM data based on the Equivalence Constraint Theory. We show that when training examples are sampled appropriately from this synthetic data and presented in certain order (aka training curriculum) along with monolingual and real CM data, it can significantly reduce the perplexity of an RNN-based language model. We also show that randomly generated CM data does not help in decreasing the perplexity of the LMs.
Discovering Implicit Knowledge with Unary Relations
Authors: Michael Glass, Alfio Gliozzo
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Deep Learning, Knowledge Base Population, Natural Language Inference, Relation, Relation Extraction, Sentence

State-of-the-art relation extraction approaches are only able to recognize relationships between mentions of entity arguments stated explicitly in the text and typically localized to the same sentence. However, the vast majority of relations are either implicit or not sententially localized. This is a major problem for Knowledge Base Population, severely limiting recall. In this paper we propose a new methodology to identify relations between two entities, consisting of detecting a very large number of unary relations, and using them to infer missing entities. We describe a deep learning architecture able to learn thousands of such relations very efficiently by using a common deep learning based representation. Our approach largely outperforms state of the art relation extraction technology on a newly introduced web scale knowledge base population benchmark, that we release to the research community.
GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data
Authors: Bayu Distiawan Trisedya, Jianzhong Qi, Rui Zhang, Wei Wang
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Data-to-Text Generation, Decoder, Question Answering, Recommendation Systems, Sentence

A knowledge base is a large repository of facts that are mainly represented as RDF triples, each of which consists of a subject, a predicate (relationship), and an object. The RDF triple representation offers a simple interface for applications to access the facts. However, this representation is not in a natural language form, which is difficult for humans to understand. We address this problem by proposing a system to translate a set of RDF triples into natural sentences based on an encoder-decoder framework. To preserve as much information from RDF triples as possible, we propose a novel graph-based triple encoder. The proposed encoder encodes not only the elements of the triples but also the relationships both within a triple and between the triples. Experimental results show that the proposed encoder achieves a consistent improvement over the baseline models by up to 17.6{\%}, 6.0{\%}, and 16.4{\%} in three common metrics BLEU, METEOR, and TER, respectively.
A Neural Approach to Pun Generation
Authors: Zhiwei Yu, Jiwei Tan, Xiaojun Wan
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Diversity, Image Captioning, Language Modeling, Language Modelling, Machine Translation, Text Generation, Text Summarization

Automatic pun generation is an interesting and challenging text generation task. Previous efforts rely on templates or laboriously manually annotated pun datasets, which heavily constrains the quality and diversity of generated puns. Since sequence-to-sequence models provide an effective technique for text generation, it is promising to investigate these models on the pun generation task. In this paper, we propose neural network models for homographic pun generation, and they can generate puns without requiring any pun data for training. We first train a conditional neural language model from a general text corpus, and then generate puns from the language model with an elaborately designed decoding algorithm. Automatic and human evaluations show that our models are able to generate homographic puns of good readability and quality.
Learning to Generate Move-by-Move Commentary for Chess Games from Large-Scale Social Forum Data
Authors: Harsh Jhamtani, Varun Gangal, Eduard Hovy, Graham Neubig, Taylor Berg-Kirkpatrick
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Game of Chess, Text Generation

This paper examines the problem of generating natural language descriptions of chess games. We introduce a new large-scale chess commentary dataset and propose methods to generate commentary for individual moves in a chess game. The introduced dataset consists of more than 298K chess move-commentary pairs across 11K chess games. We highlight how this task poses unique research challenges in natural language generation: the data contain a large variety of styles of commentary and frequently depend on pragmatic context. We benchmark various baselines and propose an end-to-end trainable neural model which takes into account multiple pragmatic aspects of the game state that may be commented upon to describe a given chess move. Through a human study on predictions for a subset of the data which deals with direct move descriptions, we observe that outputs from our models are rated similar to ground truth commentary texts in terms of correctness and fluency.
Denoising Distantly Supervised Open-Domain Question Answering
Authors: Yankai Lin, Haozhe Ji, Zhiyuan Liu, Maosong Sun
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Denoising, Information Retrieval, Open-Domain Question Answering, Question Answering, Reading Comprehension

Distantly supervised open-domain question answering (DS-QA) aims to find answers in collections of unlabeled text. Existing DS-QA models usually retrieve related paragraphs from a large-scale corpus and apply reading comprehension technique to extract answers from the most relevant paragraph. They ignore the rich information contained in other paragraphs. Moreover, distant supervision data inevitably accompanies with the wrong labeling problem, and these noisy data will substantially degrade the performance of DS-QA. To address these issues, we propose a novel DS-QA model which employs a paragraph selector to filter out those noisy paragraphs and a paragraph reader to extract the correct answer from those denoised paragraphs. Experimental results on real-world datasets show that our model can capture useful information from noisy data and achieve significant improvements on DS-QA as compared to all baselines.