pwc-archive/papers-with-abstracts huggingface dataset:
Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning
Authors: Lu Chen, Xiang Zhou, Cheng Chang, Runzhe Yang, Kai Yu
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Automatic Speech Recognition (ASR), Dialogue Management, Reinforcement Learning, Reinforcement Learning (RL), Speech Recognition, Spoken Dialogue Systems, Spoken Language Understanding

Hand-crafted rules and reinforcement learning (RL) are two popular choices to obtain dialogue policy. The rule-based policy is often reliable within predefined scope but not self-adaptable, whereas RL is evolvable with data but often suffers from a bad initial performance. We employ a \textit{companion learning} framework to integrate the two approaches for \textit{on-line} dialogue policy learning, in which a pre-defined rule-based policy acts as a {``}teacher{''} and guides a data-driven RL system by giving example actions as well as additional rewards. A novel \textit{agent-aware dropout} Deep Q-Network (AAD-DQN) is proposed to address the problem of when to consult the teacher and how to learn from the teacher{'}s experiences. AAD-DQN, as a data-driven student policy, provides (1) two separate experience memories for student and teacher, (2) an uncertainty estimated by dropout to control the timing of consultation and learning. Simulation experiments showed that the proposed approach can significantly improve both \textit{safety}and \textit{efficiency} of on-line policy optimization compared to other companion learning approaches as well as supervised pre-training using static dialogue corpus.
Towards Debate Automation: a Recurrent Model for Predicting Debate Winners
Authors: Peter Potash, Anna Rumshisky
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Text Generation

In this paper we introduce a practical first step towards the creation of an automated debate agent: a state-of-the-art recurrent predictive model for predicting debate winners. By having an accurate predictive model, we are able to objectively rate the quality of a statement made at a specific turn in a debate. The model is based on a recurrent neural network architecture with attention, which allows the model to effectively account for the entire debate when making its prediction. Our model achieves state-of-the-art accuracy on a dataset of debate transcripts annotated with audience favorability of the debate teams. Finally, we discuss how future work can leverage our proposed model for the creation of an automated debate agent. We accomplish this by determining the model input that will maximize audience favorability toward a given side of a debate at an arbitrary turn.
Further Investigation into Reference Bias in Monolingual Evaluation of Machine Translation
Authors: Qingsong Ma, Yvette Graham, Timothy Baldwin, Qun Liu
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Machine Translation, Translation

Monolingual evaluation of Machine Translation (MT) aims to simplify human assessment by requiring assessors to compare the meaning of the MT output with a reference translation, opening up the task to a much larger pool of genuinely qualified evaluators. Monolingual evaluation runs the risk, however, of bias in favour of MT systems that happen to produce translations superficially similar to the reference and, consistent with this intuition, previous investigations have concluded monolingual assessment to be strongly biased in this respect. On re-examination of past analyses, we identify a series of potential analytical errors that force some important questions to be raised about the reliability of past conclusions, however. We subsequently carry out further investigation into reference bias via direct human assessment of MT adequacy via quality controlled crowd-sourcing. Contrary to both intuition and past conclusions, results for show no significant evidence of reference bias in monolingual evaluation of MT.
Knowledge Distillation for Bilingual Dictionary Induction
Authors: Ndap Nakashole, ula, Raphael Flauger
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Knowledge Distillation, Translation, Word Embeddings, Zero-Shot Learning

Leveraging zero-shot learning to learn mapping functions between vector spaces of different languages is a promising approach to bilingual dictionary induction. However, methods using this approach have not yet achieved high accuracy on the task. In this paper, we propose a bridging approach, where our main contribution is a knowledge distillation training objective. As teachers, rich resource translation paths are exploited in this role. And as learners, translation paths involving low resource languages learn from the teachers. Our training objective allows seamless addition of teacher translation paths for any given low resource pair. Since our approach relies on the quality of monolingual word embeddings, we also propose to enhance vector representations of both the source and target language with linguistic information. Our experiments on various languages show large performance gains from our distillation training objective, obtaining as high as 17{\%} accuracy improvements.
Machine Translation, it's a question of style, innit? The case of English tag questions
Authors: Rachel Bawden
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: General Classification, Machine Translation, Multi-class Classification, TAG, Translation

In this paper, we address the problem of generating English tag questions (TQs) (e.g. it is, isn{'}t it?) in Machine Translation (MT). We propose a post-edition solution, formulating the problem as a multi-class classification task. We present (i) the automatic annotation of English TQs in a parallel corpus of subtitles and (ii) an approach using a series of classifiers to predict TQ forms, which we use to post-edit state-of-the-art MT outputs. Our method provides significant improvements in English TQ translation when translating from Czech, French and German, in turn improving the fluidity, naturalness, grammatical correctness and pragmatic coherence of MT output.
Deciphering Related Languages
Authors: Nima Pourdamghani, Kevin Knight
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Language Modeling, Language Modelling, Machine Translation

We present a method for translating texts between close language pairs. The method does not require parallel data, and it does not require the languages to be written in the same script. We show results for six language pairs: Afrikaans/Dutch, Bosnian/Serbian, Danish/Swedish, Macedonian/Bulgarian, Malaysian/Indonesian, and Polish/Belorussian. We report BLEU scores showing our method to outperform others that do not use parallel data.
Identifying Cognate Sets Across Dictionaries of Related Languages
Authors: Adam St Arnaud, David Beck, Grzegorz Kondrak
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Semantic Similarity, Semantic Textual Similarity

We present a system for identifying cognate sets across dictionaries of related languages. The likelihood of a cognate relationship is calculated on the basis of a rich set of features that capture both phonetic and semantic similarity, as well as the presence of regular sound correspondences. The similarity scores are used to cluster words from different languages that may originate from a common proto-word. When tested on the Algonquian language family, our system detects 63{\%} of cognate sets while maintaining cluster purity of 70{\%}.
Cheap Translation for Cross-Lingual Named Entity Recognition
Authors: Stephen Mayhew, Chen-Tse Tsai, Dan Roth
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Cross-Lingual NER, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Translation

Recent work in NLP has attempted to deal with low-resource languages but still assumed a resource level that is not present for most languages, e.g., the availability of Wikipedia in the target language. We propose a simple method for cross-lingual named entity recognition (NER) that works well in settings with \textit{very} minimal resources. Our approach makes use of a lexicon to {``}translate{''} annotated data available in one or several high resource language(s) into the target language, and learns a standard monolingual NER model there. Further, when Wikipedia is available in the target language, our method can enhance Wikipedia based methods to yield state-of-the-art NER results; we evaluate on 7 diverse languages, improving the state-of-the-art by an average of 5.5{\%} F1 points. With the minimal resources required, this is an extremely portable cross-lingual NER approach, as illustrated using a truly low-resource language, Uyghur.
Classification of telicity using cross-linguistic annotation projection
Authors: Annemarie Friedrich, Damyana Gateva
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Classification, General Classification, Machine Translation, Natural Language Understanding, Question Answering, Text Generation

This paper addresses the automatic recognition of telicity, an aspectual notion. A telic event includes a natural endpoint ({``}she walked home{''}), while an atelic event does not ({``}she walked around{''}). Recognizing this difference is a prerequisite for temporal natural language understanding. In English, this classification task is difficult, as telicity is a covert linguistic category. In contrast, in Slavic languages, aspect is part of a verb{'}s meaning and even available in machine-readable dictionaries. Our contributions are as follows. We successfully leverage additional silver standard training data in the form of projected annotations from parallel English-Czech data as well as context information, improving automatic telicity classification for English significantly compared to previous work. We also create a new data set of English texts manually annotated with telicity.
Counterfactual Learning from Bandit Feedback under Deterministic Logging : A Case Study in Statistical Machine Translation
Authors: Carolin Lawrence, Artem Sokolov, Stefan Riezler
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: counterfactual, Machine Translation, Structured Prediction, Translation

The goal of counterfactual learning for statistical machine translation (SMT) is to optimize a target SMT system from logged data that consist of user feedback to translations that were predicted by another, historic SMT system. A challenge arises by the fact that risk-averse commercial SMT systems deterministically log the most probable translation. The lack of sufficient exploration of the SMT output space seemingly contradicts the theoretical requirements for counterfactual learning. We show that counterfactual learning from deterministic bandit logs is possible nevertheless by smoothing out deterministic components in learning. This can be achieved by additive and multiplicative control variates that avoid degenerate behavior in empirical risk minimization. Our simulation experiments show improvements of up to 2 BLEU points by counterfactual learning from deterministic bandit feedback.
Learning Fine-grained Relations from Chinese User Generated Categories
Authors: Chengyu Wang, Yan Fan, Xiaofeng He, Aoying Zhou
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Graph Mining, Relation Extraction, Weakly-supervised Learning

User generated categories (UGCs) are short texts that reflect how people describe and organize entities, expressing rich semantic relations implicitly. While most methods on UGC relation extraction are based on pattern matching in English circumstances, learning relations from Chinese UGCs poses different challenges due to the flexibility of expressions. In this paper, we present a weakly supervised learning framework to harvest relations from Chinese UGCs. We identify is-a relations via word embedding based projection and inference, extract non-taxonomic relations and their category patterns by graph mining. We conduct experiments on Chinese Wikipedia and achieve high accuracy, outperforming state-of-the-art methods.
MinIE: Minimizing Facts in Open Information Extraction
Authors: Kiril Gashteovski, Rainer Gemulla, Luciano del Corro
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Open Information Extraction, Question Answering, Relation Extraction

The goal of Open Information Extraction (OIE) is to extract surface relations and their arguments from natural-language text in an unsupervised, domain-independent manner. In this paper, we propose MinIE, an OIE system that aims to provide useful, compact extractions with high precision and recall. MinIE approaches these goals by (1) representing information about polarity, modality, attribution, and quantities with semantic annotations instead of in the actual extraction, and (2) identifying and removing parts that are considered overly specific. We conducted an experimental study with several real-world datasets and found that MinIE achieves competitive or higher precision and recall than most prior systems, while at the same time producing shorter, semantically enriched extractions.
NITE: A Neural Inductive Teaching Framework for Domain Specific NER
Authors: Siliang Tang, Ning Zhang, Jinjiang Zhang, Fei Wu, Yueting Zhuang
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Document Classification, Multiple Instance Learning, Named Entity Recognition (NER), NER, Transfer Learning

In domain-specific NER, due to insufficient labeled training data, deep models usually fail to behave normally. In this paper, we proposed a novel Neural Inductive TEaching framework (NITE) to transfer knowledge from existing domain-specific NER models into an arbitrary deep neural network in a teacher-student training manner. NITE is a general framework that builds upon transfer learning and multiple instance learning, which collaboratively not only transfers knowledge to a deep student network but also reduces the noise from teachers. NITE can help deep learning methods to effectively utilize existing resources (i.e., models, labeled and unlabeled data) in a small domain. The experiment resulted on Disease NER proved that without using any labeled data, NITE can significantly boost the performance of a CNN-bidirectional LSTM-CRF NER neural network nearly over 30{\%} in terms of F1-score.
Speeding up Reinforcement Learning-based Information Extraction Training using Asynchronous Methods
Authors: Aditya Sharma, Zarana Parekh, Partha Talukdar
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

RLIE-DQN is a recently proposed Reinforcement Learning-based Information Extraction (IE) technique which is able to incorporate external evidence during the extraction process. RLIE-DQN trains a single agent sequentially, training on one instance at a time. This results in significant training slowdown which is undesirable. We leverage recent advances in parallel RL training using asynchronous methods and propose RLIE-A3C. RLIE-A3C trains multiple agents in parallel and is able to achieve upto 6x training speedup over RLIE-DQN, while suffering no loss in average accuracy.
Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks
Authors: Peng-Hsuan Li, Ruo-Ping Dong, Yu-Siang Wang, Ju-chieh Chou, Wei-Yun Ma
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

In this paper, we utilize the linguistic structures of texts to improve named entity recognition by BRNN-CNN, a special bidirectional recursive network attached with a convolutional network. Motivated by the observation that named entities are highly related to linguistic constituents, we propose a constituent-based BRNN-CNN for named entity recognition. In contrast to classical sequential labeling methods, the system first identifies which text chunks are possible named entities by whether they are linguistic constituents. Then it classifies these chunks with a constituency tree structure by recursively propagating syntactic and semantic information to each constituent node. This method surpasses current state-of-the-art on OntoNotes 5.0 with automatically generated parses.
Entity Linking via Joint Encoding of Types, Descriptions, and Context
Authors: Nitish Gupta, Sameer Singh, Dan Roth
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Entity Linking

For accurate entity linking, we need to capture various information aspects of an entity, such as its description in a KB, contexts in which it is mentioned, and structured knowledge. Additionally, a linking system should work on texts from different domains without requiring domain-specific training data or hand-engineered features. In this work we present a neural, modular entity linking system that learns a unified dense representation for each entity using multiple sources of information, such as its description, contexts around its mentions, and its fine-grained types. We show that the resulting entity linking system is effective at combining these sources, and performs competitively, sometimes out-performing current state-of-the-art systems across datasets, without requiring any domain-specific training data or hand-engineered features. We also show that our model can effectively {``}embed{''} entities that are new to the KB, and is able to link its mentions accurately.
An Insight Extraction System on BioMedical Literature with Deep Neural Networks
Authors: Hua He, Kris Ganjam, Navendu Jain, Jessica Lundin, Ryen White, Jimmy Lin
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Relation Extraction

Mining biomedical text offers an opportunity to automatically discover important facts and infer associations among them. As new scientific findings appear across a large collection of biomedical publications, our aim is to tap into this literature to automate biomedical knowledge extraction and identify important insights from them. Towards that goal, we develop a system with novel deep neural networks to extract insights on biomedical literature. Evaluation shows our system is able to provide insights with competitive accuracy of human acceptance and its relation extraction component outperforms previous work.
Word Etymology as Native Language Interference
Authors: Vivi Nastase, Carlo Strapparava
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9

We present experiments that show the influence of native language on lexical choice when producing text in another language {--} in this particular case English. We start from the premise that non-native English speakers will choose lexical items that are close to words in their native language. This leads us to an etymology-based representation of documents written by people whose mother tongue is an Indo-European language. Based on this representation we grow a language family tree, that matches closely the Indo-European language tree.
A Simpler and More Generalizable Story Detector using Verb and Character Features
Authors: Joshua Eisenberg, Mark Finlayson
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9

Story detection is the task of determining whether or not a unit of text contains a story. Prior approaches achieved a maximum performance of 0.66 F1, and did not generalize well across different corpora. We present a new state-of-the-art detector that achieves a maximum performance of 0.75 F1 (a 14{\%} improvement), with significantly greater generalizability than previous work. In particular, our detector achieves performance above 0.70 F1 across a variety of combinations of lexically different corpora for training and testing, as well as dramatic improvements (up to 4,000{\%}) in performance when trained on a small, disfluent data set. The new detector uses two basic types of features{--}ones related to events, and ones related to characters{--}totaling 283 specific features overall; previous detectors used tens of thousands of features, and so this detector represents a significant simplification along with increased performance.
Multi-modular domain-tailored OCR post-correction
Authors: Sarah Schulz, Jonas Kuhn
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Diversity, Machine Translation, Optical Character Recognition, Optical Character Recognition (OCR), Translation

One of the main obstacles for many Digital Humanities projects is the low data availability. Texts have to be digitized in an expensive and time consuming process whereas Optical Character Recognition (OCR) post-correction is one of the time-critical factors. At the example of OCR post-correction, we show the adaptation of a generic system to solve a specific problem with little data. The system accounts for a diversity of errors encountered in OCRed texts coming from different time periods in the domain of literature. We show that the combination of different approaches, such as e.g. Statistical Machine Translation and spell checking, with the help of a ranking mechanism tremendously improves over single-handed approaches. Since we consider the accessibility of the resulting tool as a crucial part of Digital Humanities collaborations, we describe the workflow we suggest for efficient text recognition and subsequent automatic and manual post-correction
Quantifying the Effects of Text Duplication on Semantic Models
Authors: Alex Schofield, ra, Laure Thompson, David Mimno
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Sensitivity

Duplicate documents are a pervasive problem in text datasets and can have a strong effect on unsupervised models. Methods to remove duplicate texts are typically heuristic or very expensive, so it is vital to know when and why they are needed. We measure the sensitivity of two latent semantic methods to the presence of different levels of document repetition. By artificially creating different forms of duplicate text we confirm several hypotheses about how repeated text impacts models. While a small amount of duplication is tolerable, substantial over-representation of subsets of the text may overwhelm meaningful topical patterns.
Identifying Semantically Deviating Outlier Documents
Authors: Honglei Zhuang, Chi Wang, Fangbo Tao, Lance Kaplan, Jiawei Han
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Outlier Detection

A document outlier is a document that substantially deviates in semantics from the majority ones in a corpus. Automatic identification of document outliers can be valuable in many applications, such as screening health records for medical mistakes. In this paper, we study the problem of mining semantically deviating document outliers in a given corpus. We develop a generative model to identify frequent and characteristic semantic regions in the word embedding space to represent the given corpus, and a robust outlierness measure which is resistant to noisy content in documents. Experiments conducted on two real-world textual data sets show that our method can achieve an up to 135{\%} improvement over baselines in terms of recall at top-1{\%} of the outlier ranking.
A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC
Authors: Zhuoxuan Jiang, Shanshan Feng, Gao Cong, Chunyan Miao, Xiaoming Li
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Document Ranking, Sentiment Analysis, Word Embeddings

Recent years have witnessed the proliferation of Massive Open Online Courses (MOOCs). With massive learners being offered MOOCs, there is a demand that the forum contents within MOOCs need to be classified in order to facilitate both learners and instructors. Therefore we investigate a significant application, which is to associate forum threads to subtitles of video clips. This task can be regarded as a document ranking problem, and the key is how to learn a distinguishable text representation from word sequences and learners{'} behavior sequences. In this paper, we propose a novel cascade model, which can capture both the latent semantics and latent similarity by modeling MOOC data. Experimental results on two real-world datasets demonstrate that our textual representation outperforms state-of-the-art unsupervised counterparts for the application.