mtybilly/Instruct2DS huggingface dataset:
Controlling Human Perception of Basic User Traits
Authors: Daniel Preoţiuc-Pietro, Sharath Chandra Guntuku, Lyle Ungar
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: Much of our online communication is text-mediated and, lately, more common with automated agents. Unlike interacting with humans, these agents currently do not tailor their language to the type of person they are communicating to. In this pilot study, we measure the extent to which human perception of basic user trait information -- gender and age -- is controllable through text. Using automatic models of gender and age prediction, we estimate which tweets posted by a user are more likely to mis-characterize his traits. We perform multiple controlled crowdsourcing experiments in which we show that we can reduce the human prediction accuracy of gender to almost random -- an over 20% drop in accuracy. Our experiments show that it is practically feasible for multiple applications such as text generation, text summarization or machine translation to be tailored to specific traits and perceived as such.
Topic Signatures in Political Campaign Speeches
Authors: Clément Gautrais, Peggy Cellier, René Quiniou, Alexandre Termier
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: Highlighting the recurrence of topics usage in candidates speeches is a key feature to identify the main ideas of each candidate during a political campaign. In this paper, we present a method combining standard topic modeling with signature mining for analyzing topic recurrence in speeches of Clinton and Trump during the 2016 American presidential campaign. The results show that the method extracts automatically the main ideas of each candidate and, in addition, provides information about the evolution of these topics during the campaign.
Assessing Objective Recommendation Quality through Political Forecasting
Authors: H. Andrew Schwartz, Masoud Rouhizadeh, Michael Bishop, Philip Tetlock, Barbara Mellers, Lyle Ungar
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: Recommendations are often rated for their subjective quality, but few researchers have studied comment quality in terms of objective utility. We explore recommendation quality assessment with respect to both subjective (i.e. users' ratings) and objective (i.e., did it influence? did it improve decisions?) metrics in a massive online geopolitical forecasting system, ultimately comparing linguistic characteristics of each quality metric. Using a variety of features, we predict all types of quality with better accuracy than the simple yet strong baseline of comment length. Looking at the most predictive content illustrates rater biases; for example, forecasters are subjectively biased in favor of comments mentioning business transactions or dealings as well as material things, even though such comments do not indeed prove any more useful objectively. Additionally, more complex sentence constructions, as evidenced by subordinate conjunctions, are characteristic of comments leading to objective improvements in forecasting.
Never Abandon Minorities: Exhaustive Extraction of Bursty Phrases on Microblogs Using Set Cover Problem
Authors: Masumi Shirakawa, Takahiro Hara, Takuya Maekawa
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: We propose a language-independent data-driven method to exhaustively extract bursty phrases of arbitrary forms (e.g., phrases other than simple noun phrases) from microblogs. The burst (i.e., the rapid increase of the occurrence) of a phrase causes the burst of overlapping N-grams including incomplete ones. In other words, bursty incomplete N-grams inevitably overlap bursty phrases. Thus, the proposed method performs the extraction of bursty phrases as the set cover problem in which all bursty N-grams are covered by a minimum set of bursty phrases. Experimental results using Japanese Twitter data showed that the proposed method outperformed word-based, noun phrase-based, and segmentation-based methods both in terms of accuracy and coverage.
Maximum Margin Reward Networks for Learning from Explicit and Implicit Supervision
Authors: Haoruo Peng, Ming-Wei Chang, Wen-tau Yih
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: Neural networks have achieved state-of-the-art performance on several structured-output prediction tasks, trained in a fully supervised fashion. However, annotated examples in structured domains are often costly to obtain, which thus limits the applications of neural networks. In this work, we propose Maximum Margin Reward Networks, a neural network-based framework that aims to learn from both explicit (full structures) and implicit supervision signals (delayed feedback on the correctness of the predicted structure). On named entity recognition and semantic parsing, our model outperforms previous systems on the benchmark datasets, CoNLL-2003 and WebQuestionsSP.
The Impact of Modeling Overall Argumentation with Tree Kernels
Authors: Henning Wachsmuth, Giovanni Da San Martino, Dora Kiesel, Benno Stein
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: Several approaches have been proposed to model either the explicit sequential structure of an argumentative text or its implicit hierarchical structure. So far, the adequacy of these models of overall argumentation remains unclear. This paper asks what type of structure is actually important to tackle downstream tasks in computational argumentation. We analyze patterns in the overall argumentation of texts from three corpora. Then, we adapt the idea of positional tree kernels in order to capture sequential and hierarchical argumentative structure together for the first time. In systematic experiments for three text classification tasks, we find strong evidence for the impact of both types of structure. Our results suggest that either of them is necessary while their combination may be beneficial.
Learning Generic Sentence Representations Using Convolutional Neural Networks
Authors: Zhe Gan, Yunchen Pu, Ricardo Henao, Chunyuan Li, Xiaodong He, Lawrence Carin
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: We propose a new encoder-decoder approach to learn distributed sentence representations that are applicable to multiple purposes. The model is learned by using a convolutional neural network as an encoder to map an input sentence into a continuous vector, and using a long short-term memory recurrent neural network as a decoder. Several tasks are considered, including sentence reconstruction and future sentence prediction. Further, a hierarchical encoder-decoder model is proposed to encode a sentence to predict multiple future sentences. By training our models on a large collection of novels, we obtain a highly generic convolutional sentence encoder that performs well in practice. Experimental results on several benchmark datasets, and across a broad range of applications, demonstrate the superiority of the proposed model over competing methods.
Repeat before Forgetting: Spaced Repetition for Efficient and Effective Training of Neural Networks
Authors: Hadi Amiri, Timothy Miller, Guergana Savova
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: We present a novel approach for training artificial neural networks. Our approach is inspired by broad evidence in psychology that shows human learners can learn efficiently and effectively by increasing intervals of time between subsequent reviews of previously learned materials (spaced repetition). We investigate the analogy between training neural models and findings in psychology about human memory model and develop an efficient and effective algorithm to train neural models. The core part of our algorithm is a cognitively-motivated scheduler according to which training instances and their “reviews” are spaced over time. Our algorithm uses only 34-50% of data per epoch, is 2.9-4.8 times faster than standard training, and outperforms competing state-of-the-art baselines. Our code is available at r̆lscholar.harvard.edu/hadi/RbF/.
Part-of-Speech Tagging for Twitter with Adversarial Neural Networks
Authors: Tao Gui, Qi Zhang, Haoran Huang, Minlong Peng, Xuanjing Huang
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: In this work, we study the problem of part-of-speech tagging for Tweets. In contrast to newswire articles, Tweets are usually informal and contain numerous out-of-vocabulary words. Moreover, there is a lack of large scale labeled datasets for this domain. To tackle these challenges, we propose a novel neural network to make use of out-of-domain labeled data, unlabeled in-domain data, and labeled in-domain data. Inspired by adversarial neural networks, the proposed method tries to learn common features through adversarial discriminator. In addition, we hypothesize that domain-specific features of target domain should be preserved in some degree. Hence, the proposed method adopts a sequence-to-sequence autoencoder to perform this task. Experimental results on three different datasets show that our method achieves better performance than state-of-the-art methods.
Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings
Authors: Bofang Li, Tao Liu, Zhe Zhao, Buzhou Tang, Aleksandr Drozd, Anna Rogers, Xiaoyong Du
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: The number of word embedding models is growing every year. Most of them are based on the co-occurrence information of words and their contexts. However, it is still an open question what is the best definition of context. We provide a systematical investigation of 4 different syntactic context types and context representations for learning word embeddings. Comprehensive experiments are conducted to evaluate their effectiveness on 6 extrinsic and intrinsic tasks. We hope that this paper, along with the published code, would be helpful for choosing the best context type and representation for a given task.
Does syntax help discourse segmentation? Not so much
Authors: Chloé Braud, Ophélie Lacroix, Anders Søgaard
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: Discourse segmentation is the first step in building discourse parsers. Most work on discourse segmentation does not scale to real-world discourse parsing across languages, for two reasons: (i) models rely on constituent trees, and (ii) experiments have relied on gold standard identification of sentence and token boundaries. We therefore investigate to what extent constituents can be replaced with universal dependencies, or left out completely, as well as how state-of-the-art segmenters fare in the absence of sentence boundaries. Our results show that dependency information is less useful than expected, but we provide a fully scalable, robust model that only relies on part-of-speech information, and show that it performs well across languages in the absence of any gold-standard annotation.
Deal or No Deal? End-to-End Learning of Negotiation Dialogues
Authors: Mike Lewis, Denis Yarats, Yann Dauphin, Devi Parikh, Dhruv Batra
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions. Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interesting task for AI. We gather a large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other`s reward functions must reach an agreement (or a deal) via natural language dialogue. For the first time, we show it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states. We also introduce dialogue rollouts, in which the model plans ahead by simulating possible complete continuations of the conversation, and find that this technique dramatically improves performance. Our code and dataset are publicly available.
Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning
Authors: Lu Chen, Xiang Zhou, Cheng Chang, Runzhe Yang, Kai Yu
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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 \textitcompanion learning framework to integrate the two approaches for \textiton-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 \textitagent-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 \textitsafetyand \textitefficiency 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
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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.
A Challenge Set Approach to Evaluating Machine Translation
Authors: Pierre Isabelle, Colin Cherry, George Foster
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: Neural machine translation represents an exciting leap forward in translation quality. But what longstanding weaknesses does it resolve, and which remain? We address these questions with a challenge set approach to translation evaluation and error analysis. A challenge set consists of a small set of sentences, each hand-designed to probe a system`s capacity to bridge a particular structural divergence between languages. To exemplify this approach, we present an English-French challenge set, and use it to analyze phrase-based and neural systems. The resulting analysis provides not only a more fine-grained picture of the strengths of neural systems, but also insight into which linguistic phenomena remain out of reach.
Knowledge Distillation for Bilingual Dictionary Induction
Authors: Ndapandula Nakashole, Raphael Flauger
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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.
Deciphering Related Languages
Authors: Nima Pourdamghani, Kevin Knight
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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%.
Learning Language Representations for Typology Prediction
Authors: Chaitanya Malaviya, Graham Neubig, Patrick Littell
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: One central mystery of neural NLP is what neural models “know” about their subject matter. When a neural machine translation system learns to translate from one language to another, does it learn the syntax or semantics of the languages? Can this knowledge be extracted from the system to fill holes in human scientific knowledge? Existing typological databases contain relatively full feature specifications for only a few hundred languages. Exploiting the existence of parallel texts in more than a thousand languages, we build a massive many-to-one NMT system from 1017 languages into English, and use this to predict information missing from typological databases. Experiments show that the proposed method is able to infer not only syntactic, but also phonological and phonetic inventory features, and improves over a baseline that has access to information about the languages geographic and phylogenetic neighbors.
Cheap Translation for Cross-Lingual Named Entity Recognition
Authors: Stephen Mayhew, Chen-Tse Tsai, Dan Roth
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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 \textitvery 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.
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation
Authors: Ivan Vulić, ́ Nikola Mrkši\, Anna Korhonen
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: Existing approaches to automatic VerbNet-style verb classification are heavily dependent on feature engineering and therefore limited to languages with mature NLP pipelines. In this work, we propose a novel cross-lingual transfer method for inducing VerbNets for multiple languages. To the best of our knowledge, this is the first study which demonstrates how the architectures for learning word embeddings can be applied to this challenging syntactic-semantic task. Our method uses cross-lingual translation pairs to tie each of the six target languages into a bilingual vector space with English, jointly specialising the representations to encode the relational information from English VerbNet. A standard clustering algorithm is then run on top of the VerbNet-specialised representations, using vector dimensions as features for learning verb classes. Our results show that the proposed cross-lingual transfer approach sets new state-of-the-art verb classification performance across all six target languages explored in this work.
Classification of telicity using cross-linguistic annotation projection
Authors: Annemarie Friedrich, Damyana Gateva
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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.
Improving Slot Filling Performance with Attentive Neural Networks on Dependency Structures
Authors: Lifu Huang, Avirup Sil, Heng Ji, Radu Florian
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: Slot Filling (SF) aims to extract the values of certain types of attributes (or slots, such as person:cities_of_residence) for a given entity from a large collection of source documents. In this paper we propose an effective DNN architecture for SF with the following new strategies: (1). Take a regularized dependency graph instead of a raw sentence as input to DNN, to compress the wide contexts between query and candidate filler; (2). Incorporate two attention mechanisms: local attention learned from query and candidate filler, and global attention learned from external knowledge bases, to guide the model to better select indicative contexts to determine slot type. Experiments show that this framework outperforms state-of-the-art on both relation extraction (16% absolute F-score gain) and slot filling validation for each individual system (up to 8.5% absolute F-score gain).
Identifying Products in Online Cybercrime Marketplaces: A Dataset for Fine-grained Domain Adaptation
Authors: Greg Durrett, Jonathan K. Kummerfeld, Taylor Berg-Kirkpatrick, Rebecca Portnoff, Sadia Afroz, Damon McCoy, Kirill Levchenko, Vern Paxson
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: One weakness of machine-learned NLP models is that they typically perform poorly on out-of-domain data. In this work, we study the task of identifying products being bought and sold in online cybercrime forums, which exhibits particularly challenging cross-domain effects. We formulate a task that represents a hybrid of slot-filling information extraction and named entity recognition and annotate data from four different forums. Each of these forums constitutes its own “fine-grained domain” in that the forums cover different market sectors with different properties, even though all forums are in the broad domain of cybercrime. We characterize these domain differences in the context of a learning-based system: supervised models see decreased accuracy when applied to new forums, and standard techniques for semi-supervised learning and domain adaptation have limited effectiveness on this data, which suggests the need to improve these techniques. We release a dataset of 1,938 annotated posts from across the four forums.
Labeling Gaps Between Words: Recognizing Overlapping Mentions with Mention Separators
Authors: Aldrian Obaja Muis, Wei Lu
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: In this paper, we propose a new model that is capable of recognizing overlapping mentions. We introduce a novel notion of mention separators that can be effectively used to capture how mentions overlap with one another. On top of a novel multigraph representation that we introduce, we show that efficient and exact inference can still be performed. We present some theoretical analysis on the differences between our model and a recently proposed model for recognizing overlapping mentions, and discuss the possible implications of the differences. Through extensive empirical analysis on standard datasets, we demonstrate the effectiveness of our approach.
Deep Joint Entity Disambiguation with Local Neural Attention
Authors: Octavian-Eugen Ganea, Thomas Hofmann
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: We propose a novel deep learning model for joint document-level entity disambiguation, which leverages learned neural representations. Key components are entity embeddings, a neural attention mechanism over local context windows, and a differentiable joint inference stage for disambiguation. Our approach thereby combines benefits of deep learning with more traditional approaches such as graphical models and probabilistic mention-entity maps. Extensive experiments show that we are able to obtain competitive or state-of-the-art accuracy at moderate computational costs.
MinIE: Minimizing Facts in Open Information Extraction
Authors: Kiril Gashteovski, Rainer Gemulla, Luciano del Corro
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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.
Scientific Information Extraction with Semi-supervised Neural Tagging
Authors: Yi Luan, Mari Ostendorf, Hannaneh Hajishirzi
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: This paper addresses the problem of extracting keyphrases from scientific articles and categorizing them as corresponding to a task, process, or material. We cast the problem as sequence tagging and introduce semi-supervised methods to a neural tagging model, which builds on recent advances in named entity recognition. Since annotated training data is scarce in this domain, we introduce a graph-based semi-supervised algorithm together with a data selection scheme to leverage unannotated articles. Both inductive and transductive semi-supervised learning strategies outperform state-of-the-art information extraction performance on the 2017 SemEval Task 10 ScienceIE task.
NITE: A Neural Inductive Teaching Framework for Domain Specific NER
Authors: Siliang Tang, Ning Zhang, Jinjiang Zhang, Fei Wu, Yueting Zhuang
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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.
Fast and Accurate Entity Recognition with Iterated Dilated Convolutions
Authors: Emma Strubell, Patrick Verga, David Belanger, Andrew McCallum
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs. Recent advances in GPU hardware have led to the emergence of bi-directional LSTMs as a standard method for obtaining per-token vector representations serving as input to labeling tasks such as NER (often followed by prediction in a linear-chain CRF). Though expressive and accurate, these models fail to fully exploit GPU parallelism, limiting their computational efficiency. This paper proposes a faster alternative to Bi-LSTMs for NER: Iterated Dilated Convolutional Neural Networks (ID-CNNs), which have better capacity than traditional CNNs for large context and structured prediction. Unlike LSTMs whose sequential processing on sentences of length N requires O(N) time even in the face of parallelism, ID-CNNs permit fixed-depth convolutions to run in parallel across entire documents. We describe a distinct combination of network structure, parameter sharing and training procedures that enable dramatic 14-20x test-time speedups while retaining accuracy comparable to the Bi-LSTM-CRF. Moreover, ID-CNNs trained to aggregate context from the entire document are more accurate than Bi-LSTM-CRFs while attaining 8x faster test time speeds.
Entity Linking via Joint Encoding of Types, Descriptions, and Context
Authors: Nitish Gupta, Sameer Singh, Dan Roth
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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
Learning to Predict Charges for Criminal Cases with Legal Basis
Authors: Bingfeng Luo, Yansong Feng, Jianbo Xu, Xiang Zhang, Dongyan Zhao
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: The charge prediction task is to determine appropriate charges for a given case, which is helpful for legal assistant systems where the user input is fact description. We argue that relevant law articles play an important role in this task, and therefore propose an attention-based neural network method to jointly model the charge prediction task and the relevant article extraction task in a unified framework. The experimental results show that, besides providing legal basis, the relevant articles can also clearly improve the charge prediction results, and our full model can effectively predict appropriate charges for cases with different expression styles.
Quantifying the Effects of Text Duplication on Semantic Models
Authors: Alexandra Schofield, Laure Thompson, David Mimno
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: 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.
Detecting and Explaining Causes From Text For a Time Series Event
Authors: Dongyeop Kang, Varun Gangal, Ang Lu, Zheng Chen, Eduard Hovy
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2017

Abstract: Explaining underlying causes or effects about events is a challenging but valuable task. We define a novel problem of generating explanations of a time series event by (1) searching cause and effect relationships of the time series with textual data and (2) constructing a connecting chain between them to generate an explanation. To detect causal features from text, we propose a novel method based on the Granger causality of time series between features extracted from text such as N-grams, topics, sentiments, and their composition. The generation of the sequence of causal entities requires a commonsense causative knowledge base with efficient reasoning. To ensure good interpretability and appropriate lexical usage we combine symbolic and neural representations, using a neural reasoning algorithm trained on commonsense causal tuples to predict the next cause step. Our quantitative and human analysis show empirical evidence that our method successfully extracts meaningful causality relationships between time series with textual features and generates appropriate explanation between them.