pwc-archive/papers-with-abstracts huggingface dataset:
Generating Natural Language Question-Answer Pairs from a Knowledge Graph Using a RNN Based Question Generation Model
Authors: Sathish Reddy, Dinesh Raghu, Mitesh M. Khapra, Sachindra Joshi
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Knowledge Graphs, Question Answering, Question Generation, Question-Generation

In recent years, knowledge graphs such as Freebase that capture facts about entities and relationships between them have been used actively for answering factoid questions. In this paper, we explore the problem of automatically generating question answer pairs from a given knowledge graph. The generated question answer (QA) pairs can be used in several downstream applications. For example, they could be used for training better QA systems. To generate such QA pairs, we first extract a set of keywords from entities and relationships expressed in a triple stored in the knowledge graph. From each such set, we use a subset of keywords to generate a natural language question that has a unique answer. We treat this subset of keywords as a sequence and propose a sequence to sequence model using RNN to generate a natural language question from it. Our RNN based model generates QA pairs with an accuracy of 33.61 percent and performs 110.47 percent (relative) better than a state-of-the-art template based method for generating natural language question from keywords. We also do an extrinsic evaluation by using the generated QA pairs to train a QA system and observe that the F1-score of the QA system improves by 5.5 percent (relative) when using automatically generated QA pairs in addition to manually generated QA pairs available for training.
Efficient Benchmarking of NLP APIs using Multi-armed Bandits
Authors: Gholamreza Haffari, Tuan Dung Tran, Mark Carman
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Benchmarking, Multi-Armed Bandits, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Thompson Sampling, Two-sample testing

Comparing NLP systems to select the best one for a task of interest, such as named entity recognition, is critical for practitioners and researchers. A rigorous approach involves setting up a hypothesis testing scenario using the performance of the systems on query documents. However, often the hypothesis testing approach needs to send a lot of document queries to the systems, which can be problematic. In this paper, we present an effective alternative based on the multi-armed bandit (MAB). We propose a hierarchical generative model to represent the uncertainty in the performance measures of the competing systems, to be used by Thompson Sampling to solve the resulting MAB. Experimental results on both synthetic and real data show that our approach requires significantly fewer queries compared to the standard benchmarking technique to identify the best system according to F-measure.
A Hierarchical Neural Model for Learning Sequences of Dialogue Acts
Authors: Quan Hung Tran, Ingrid Zukerman, Gholamreza Haffari
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Automatic Speech Recognition (ASR), Machine Translation, Part-Of-Speech Tagging, Speech Recognition

We propose a novel hierarchical Recurrent Neural Network (RNN) for learning sequences of Dialogue Acts (DAs). The input in this task is a sequence of utterances (i.e., conversational contributions) comprising a sequence of tokens, and the output is a sequence of DA labels (one label per utterance). Our model leverages the hierarchical nature of dialogue data by using two nested RNNs that capture long-range dependencies at the dialogue level and the utterance level. This model is combined with an attention mechanism that focuses on salient tokens in utterances. Our experimental results show that our model outperforms strong baselines on two popular datasets, Switchboard and MapTask; and our detailed empirical analysis highlights the impact of each aspect of our model.
May I take your order? A Neural Model for Extracting Structured Information from Conversations
Authors: Baolin Peng, Michael Seltzer, Y.C. Ju, Geoffrey Zweig, Kam-Fai Wong
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Machine Translation, Translation

In this paper we tackle a unique and important problem of extracting a structured order from the conversation a customer has with an order taker at a restaurant. This is motivated by an actual system under development to assist in the order taking process. We develop a sequence-to-sequence model that is able to map from unstructured conversational input to the structured form that is conveyed to the kitchen and appears on the customer receipt. This problem is critically different from other tasks like machine translation where sequence-to-sequence models have been used: the input includes two sides of a conversation; the output is highly structured; and logical manipulations must be performed, for example when the customer changes his mind while ordering. We present a novel sequence-to-sequence model that incorporates a special attention-memory gating mechanism and conversational role markers. The proposed model improves performance over both a phrase-based machine translation approach and a standard sequence-to-sequence model.
A Two-stage Sieve Approach for Quote Attribution
Authors: Grace Muzny, Michael Fang, Angel Chang, Dan Jurafsky
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Vocal Bursts Valence Prediction

We present a deterministic sieve-based system for attributing quotations in literary text and a new dataset: QuoteLi3. Quote attribution, determining who said what in a given text, is important for tasks like creating dialogue systems, and in newer areas like computational literary studies, where it creates opportunities to analyze novels at scale rather than only a few at a time. We release QuoteLi3, which contains more than 6,000 annotations linking quotes to speaker mentions and quotes to speaker entities, and introduce a new algorithm for quote attribution. Our two-stage algorithm first links quotes to mentions, then mentions to entities. Using two stages encapsulates difficult sub-problems and improves system performance. The modular design allows us to tune for overall performance or higher precision, which is useful for many real-world use cases. Our system achieves an average F-score of 87.5 across three novels, outperforming previous systems, and can be tuned for precision of 90.4 at a recall of 65.1.
Out-of-domain FrameNet Semantic Role Labeling
Authors: Silvana Hartmann, Ilia Kuznetsov, Teresa Martin, Iryna Gurevych
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Semantic Role Labeling

Domain dependence of NLP systems is one of the major obstacles to their application in large-scale text analysis, also restricting the applicability of FrameNet semantic role labeling (SRL) systems. Yet, current FrameNet SRL systems are still only evaluated on a single in-domain test set. For the first time, we study the domain dependence of FrameNet SRL on a wide range of benchmark sets. We create a novel test set for FrameNet SRL based on user-generated web text and find that the major bottleneck for out-of-domain FrameNet SRL is the frame identification step. To address this problem, we develop a simple, yet efficient system based on distributed word representations. Our system closely approaches the state-of-the-art in-domain while outperforming the best available frame identification system out-of-domain. We publish our system and test data for research purposes.
TDParse: Multi-target-specific sentiment recognition on Twitter
Authors: Bo Wang, Maria Liakata, Arkaitz Zubiaga, Rob Procter
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Dependency Parsing, Opinion Mining, Sentiment Analysis, Twitter Sentiment Analysis

Existing target-specific sentiment recognition methods consider only a single target per tweet, and have been shown to miss nearly half of the actual targets mentioned. We present a corpus of UK election tweets, with an average of 3.09 entities per tweet and more than one type of sentiment in half of the tweets. This requires a method for multi-target specific sentiment recognition, which we develop by using the context around a target as well as syntactic dependencies involving the target. We present results of our method on both a benchmark corpus of single targets and the multi-target election corpus, showing state-of-the art performance in both corpora and outperforming previous approaches to multi-target sentiment task as well as deep learning models for single-target sentiment.
An Extensive Empirical Evaluation of Character-Based Morphological Tagging for 14 Languages
Authors: Georg Heigold, Guenter Neumann, Josef van Genabith
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Language Modeling, Language Modelling, Machine Translation, Morphological Analysis, Morphological Tagging, Named Entity Recognition (NER), Part-Of-Speech Tagging, TAG

This paper investigates neural character-based morphological tagging for languages with complex morphology and large tag sets. Character-based approaches are attractive as they can handle rarely- and unseen words gracefully. We evaluate on 14 languages and observe consistent gains over a state-of-the-art morphological tagger across all languages except for English and French, where we match the state-of-the-art. We compare two architectures for computing character-based word vectors using recurrent (RNN) and convolutional (CNN) nets. We show that the CNN based approach performs slightly worse and less consistently than the RNN based approach. Small but systematic gains are observed when combining the two architectures by ensembling.
Online Automatic Post-editing for MT in a Multi-Domain Translation Environment
Authors: Rajen Chatterjee, Gebremedhen Gebremelak, Matteo Negri, Marco Turchi
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Automatic Post-Editing, Decoder, Diversity, Machine Translation, Translation

Automatic post-editing (APE) for machine translation (MT) aims to fix recurrent errors made by the MT decoder by learning from correction examples. In controlled evaluation scenarios, the representativeness of the training set with respect to the test data is a key factor to achieve good performance. Real-life scenarios, however, do not guarantee such favorable learning conditions. Ideally, to be integrated in a real professional translation workflow (e.g. to play a role in computer-assisted translation framework), APE tools should be flexible enough to cope with continuous streams of diverse data coming from different domains/genres. To cope with this problem, we propose an online APE framework that is: i) robust to data diversity (i.e. capable to learn and apply correction rules in the right contexts) and ii) able to evolve over time (by continuously extending and refining its knowledge). In a comparative evaluation, with English-German test data coming in random order from two different domains, we show the effectiveness of our approach, which outperforms a strong batch system and the state of the art in online APE.
Integrated Learning of Dialog Strategies and Semantic Parsing
Authors: Aishwarya Padmakumar, Jesse Thomason, Raymond J. Mooney
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: BIG-bench Machine Learning, Management, Natural Language Understanding, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Semantic Parsing

Natural language understanding and dialog management are two integral components of interactive dialog systems. Previous research has used machine learning techniques to individually optimize these components, with different forms of direct and indirect supervision. We present an approach to integrate the learning of both a dialog strategy using reinforcement learning, and a semantic parser for robust natural language understanding, using only natural dialog interaction for supervision. Experimental results on a simulated task of robot instruction demonstrate that joint learning of both components improves dialog performance over learning either of these components alone.
Unsupervised AMR-Dependency Parse Alignment
Authors: Wei-Te Chen, Martha Palmer
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Abstract Meaning Representation, AMR Parsing

In this paper, we introduce an Abstract Meaning Representation (AMR) to Dependency Parse aligner. Alignment is a preliminary step for AMR parsing, and our aligner improves current AMR parser performance. Our aligner involves several different features, including named entity tags and semantic role labels, and uses Expectation-Maximization training. Results show that our aligner reaches an 87.1{\%} F-Score score with the experimental data, and enhances AMR parsing.
Improving Chinese Semantic Role Labeling using High-quality Surface and Deep Case Frames
Authors: Gongye Jin, Daisuke Kawahara, Sadao Kurohashi
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Chinese Semantic Role Labeling, Dependency Parsing, Machine Translation, Management, Question Answering, Semantic Role Labeling

This paper presents a method for applying automatically acquired knowledge to semantic role labeling (SRL). We use a large amount of automatically extracted knowledge to improve the performance of SRL. We present two varieties of knowledge, which we call surface case frames and deep case frames. Although the surface case frames are compiled from syntactic parses and can be used as rich syntactic knowledge, they have limited capability for resolving semantic ambiguity. To compensate the deficiency of the surface case frames, we compile deep case frames from automatic semantic roles. We also consider quality management for both types of knowledge in order to get rid of the noise brought from the automatic analyses. The experimental results show that Chinese SRL can be improved using automatically acquired knowledge and the quality management shows a positive effect on this task.
The ContrastMedium Algorithm: Taxonomy Induction From Noisy Knowledge Graphs With Just A Few Links
Authors: Stefano Faralli, Alex Panchenko, er, Chris Biemann, Simone Paolo Ponzetto
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Knowledge Graphs, Open Information Extraction

In this paper, we present ContrastMedium, an algorithm that transforms noisy semantic networks into full-fledged, clean taxonomies. ContrastMedium is able to identify the embedded taxonomy structure from a noisy knowledge graph without explicit human supervision such as, for instance, a set of manually selected input root and leaf concepts. This is achieved by leveraging structural information from a companion reference taxonomy, to which the input knowledge graph is linked (either automatically or manually). When used in conjunction with methods for hypernym acquisition and knowledge base linking, our methodology provides a complete solution for end-to-end taxonomy induction. We conduct experiments using automatically acquired knowledge graphs, as well as a SemEval benchmark, and show that our method is able to achieve high performance on the task of taxonomy induction.
Probabilistic Inference for Cold Start Knowledge Base Population with Prior World Knowledge
Authors: Bonan Min, Marjorie Freedman, Talya Meltzer
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Common Sense Reasoning, Entity Linking, Knowledge Base Population, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Question Answering, Relation, Relation Extraction, World Knowledge

Building knowledge bases (KB) automatically from text corpora is crucial for many applications such as question answering and web search. The problem is very challenging and has been divided into sub-problems such as mention and named entity recognition, entity linking and relation extraction. However, combining these components has shown to be under-constrained and often produces KBs with supersize entities and common-sense errors in relations (a person has multiple birthdates). The errors are difficult to resolve solely with IE tools but become obvious with world knowledge at the corpus level. By analyzing Freebase and a large text collection, we found that per-relation cardinality and the popularity of entities follow the power-law distribution favoring flat long tails with low-frequency instances. We present a probabilistic joint inference algorithm to incorporate this world knowledge during KB construction. Our approach yields state-of-the-art performance on the TAC Cold Start task, and 42{\%} and 19.4{\%} relative improvements in F1 over our baseline on Cold Start hop-1 and all-hop queries respectively.
Learning to Generate Product Reviews from Attributes
Authors: Li Dong, Shaohan Huang, Furu Wei, Mirella Lapata, Ming Zhou, Ke Xu
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Attribute, Decoder, Review Generation, Sentiment Analysis, Text Generation

Automatically generating product reviews is a meaningful, yet not well-studied task in sentiment analysis. Traditional natural language generation methods rely extensively on hand-crafted rules and predefined templates. This paper presents an attention-enhanced attribute-to-sequence model to generate product reviews for given attribute information, such as user, product, and rating. The attribute encoder learns to represent input attributes as vectors. Then, the sequence decoder generates reviews by conditioning its output on these vectors. We also introduce an attention mechanism to jointly generate reviews and align words with input attributes. The proposed model is trained end-to-end to maximize the likelihood of target product reviews given the attributes. We build a publicly available dataset for the review generation task by leveraging the Amazon book reviews and their metadata. Experiments on the dataset show that our approach outperforms baseline methods and the attention mechanism significantly improves the performance of our model.
Generating flexible proper name references in text: Data, models and evaluation
Authors: Thiago Castro Ferreira, Emiel Krahmer, S Wubben, er
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Text Generation

This study introduces a statistical model able to generate variations of a proper name by taking into account the person to be mentioned, the discourse context and variation. The model relies on the REGnames corpus, a dataset with 53,102 proper name references to 1,000 people in different discourse contexts. We evaluate the versions of our model from the perspective of how human writers produce proper names, and also how human readers process them. The corpus and the model are publicly available.
Noisy-context surprisal as a human sentence processing cost model
Authors: Richard Futrell, Roger Levy
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Sentence

We use the noisy-channel theory of human sentence comprehension to develop an incremental processing cost model that unifies and extends key features of expectation-based and memory-based models. In this model, which we call noisy-context surprisal, the processing cost of a word is the surprisal of the word given a noisy representation of the preceding context. We show that this model accounts for an outstanding puzzle in sentence comprehension, language-dependent structural forgetting effects (Gibson and Thomas, 1999; Vasishth et al., 2010; Frank et al., 2016), which are previously not well modeled by either expectation-based or memory-based approaches. Additionally, we show that this model derives and generalizes locality effects (Gibson, 1998; Demberg and Keller, 2008), a signature prediction of memory-based models. We give corpus-based evidence for a key assumption in this derivation.
On-demand Injection of Lexical Knowledge for Recognising Textual Entailment
Authors: Pascual Mart{\'\i}nez-G{\'o}mez, Koji Mineshima, Yusuke Miyao, Daisuke Bekki
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Automated Theorem Proving, Information Retrieval, Natural Language Inference, Question Answering

We approach the recognition of textual entailment using logical semantic representations and a theorem prover. In this setup, lexical divergences that preserve semantic entailment between the source and target texts need to be explicitly stated. However, recognising subsentential semantic relations is not trivial. We address this problem by monitoring the proof of the theorem and detecting unprovable sub-goals that share predicate arguments with logical premises. If a linguistic relation exists, then an appropriate axiom is constructed on-demand and the theorem proving continues. Experiments show that this approach is effective and precise, producing a system that outperforms other logic-based systems and is competitive with state-of-the-art statistical methods.
Learning to Predict Denotational Probabilities For Modeling Entailment
Authors: Alice Lai, Julia Hockenmaier
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Coreference Resolution, Natural Language Inference

We propose a framework that captures the denotational probabilities of words and phrases by embedding them in a vector space, and present a method to induce such an embedding from a dataset of denotational probabilities. We show that our model successfully predicts denotational probabilities for unseen phrases, and that its predictions are useful for textual entailment datasets such as SICK and SNLI.
A Societal Sentiment Analysis: Predicting the Values and Ethics of Individuals by Analysing Social Media Content
Authors: Tushar Maheshwari, Aishwarya N. Reganti, Samiksha Gupta, Anupam Jamatia, Upendra Kumar, Bj{\"o}rn Gamb{\"a}ck, Amitava Das
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Ethics, General Classification, regression, Sentiment Analysis, Topic Models

To find out how users{'} social media behaviour and language are related to their ethical practices, the paper investigates applying Schwartz{'} psycholinguistic model of societal sentiment to social media text. The analysis is based on corpora collected from user essays as well as social media (Facebook and Twitter). Several experiments were carried out on the corpora to classify the ethical values of users, incorporating Linguistic Inquiry Word Count analysis, n-grams, topic models, psycholinguistic lexica, speech-acts, and non-linguistic information, while applying a range of machine learners (Support Vector Machines, Logistic Regression, and Random Forests) to identify the best linguistic and non-linguistic features for automatic classification of values and ethics.
Online Learning of Task-specific Word Representations with a Joint Biconvex Passive-Aggressive Algorithm
Authors: Pascal Denis, Liva Ralaivola
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: General Classification, Sentiment Analysis, text-classification, Text Classification, Word Embeddings

This paper presents a new, efficient method for learning task-specific word vectors using a variant of the Passive-Aggressive algorithm. Specifically, this algorithm learns a word embedding matrix in tandem with the classifier parameters in an online fashion, solving a bi-convex constrained optimization at each iteration. We provide a theoretical analysis of this new algorithm in terms of regret bounds, and evaluate it on both synthetic data and NLP classification problems, including text classification and sentiment analysis. In the latter case, we compare various pre-trained word vectors to initialize our word embedding matrix, and show that the matrix learned by our algorithm vastly outperforms the initial matrix, with performance results comparable or above the state-of-the-art on these tasks.