pwc-archive/papers-with-abstracts huggingface dataset:
Sentiment Lexicon Expansion Based on Neural PU Learning, Double Dictionary Lookup, and Polarity Association
Authors: Yasheng Wang, Yang Zhang, Bing Liu
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: General Classification, Sentiment Analysis

Although many sentiment lexicons in different languages exist, most are not comprehensive. In a recent sentiment analysis application, we used a large Chinese sentiment lexicon and found that it missed a large number of sentiment words in social media. This prompted us to make a new attempt to study sentiment lexicon expansion. This paper first poses the problem as a PU learning problem, which is a new formulation. It then proposes a new PU learning method suitable for our problem using a neural network. The results are enhanced further with a new dictionary-based technique and a novel polarity classification technique. Experimental results show that the proposed approach outperforms baseline methods greatly.
Neural Response Generation via GAN with an Approximate Embedding Layer
Authors: Zhen Xu, Bingquan Liu, Baoxun Wang, Chengjie Sun, Xiaolong Wang, Zhuoran Wang, Chao Qi
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Diversity, Generative Adversarial Network, Machine Translation, Response Generation

This paper presents a Generative Adversarial Network (GAN) to model single-turn short-text conversations, which trains a sequence-to-sequence (Seq2Seq) network for response generation simultaneously with a discriminative classifier that measures the differences between human-produced responses and machine-generated ones. In addition, the proposed method introduces an approximate embedding layer to solve the non-differentiable problem caused by the sampling-based output decoding procedure in the Seq2Seq generative model. The GAN setup provides an effective way to avoid noninformative responses (a.k.a {``}safe responses{''}), which are frequently observed in traditional neural response generators. The experimental results show that the proposed approach significantly outperforms existing neural response generation models in diversity metrics, with slight increases in relevance scores as well, when evaluated on both a Mandarin corpus and an English corpus.
Filling the Blanks (hint: plural noun) for Mad Libs Humor
Authors: Nabil Hossain, John Krumm, V, Lucy erwende, Eric Horvitz, Henry Kautz
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9

Computerized generation of humor is a notoriously difficult AI problem. We develop an algorithm called Libitum that helps humans generate humor in a Mad Lib, which is a popular fill-in-the-blank game. The algorithm is based on a machine learned classifier that determines whether a potential fill-in word is funny in the context of the Mad Lib story. We use Amazon Mechanical Turk to create ground truth data and to judge humor for our classifier to mimic, and we make this data freely available. Our testing shows that Libitum successfully aids humans in filling in Mad Libs that are usually judged funnier than those filled in by humans with no computerized help. We go on to analyze why some words are better than others at making a Mad Lib funny.
Multi-Grained Chinese Word Segmentation
Authors: Chen Gong, Zhenghua Li, Min Zhang, Xinzhou Jiang
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Chinese Word Segmentation, Language Modeling, Language Modelling, Segmentation, Sentence

Traditionally, word segmentation (WS) adopts the single-grained formalism, where a sentence corresponds to a single word sequence. However, Sproat et al. (1997) show that the inter-native-speaker consistency ratio over Chinese word boundaries is only 76{\%}, indicating single-grained WS (SWS) imposes unnecessary challenges on both manual annotation and statistical modeling. Moreover, WS results of different granularities can be complementary and beneficial for high-level applications. This work proposes and addresses multi-grained WS (MWS). We build a large-scale pseudo MWS dataset for model training and tuning by leveraging the annotation heterogeneity of three SWS datasets. Then we manually annotate 1,500 test sentences with true MWS annotations. Finally, we propose three benchmark approaches by casting MWS as constituent parsing and sequence labeling. Experiments and analysis lead to many interesting findings.
Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic
Authors: Nasser Zalmout, Nizar Habash
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Feature Engineering, Language Modeling, Language Modelling, Morphological Analysis, Morphological Disambiguation, Morphological Tagging

This paper presents a model for Arabic morphological disambiguation based on Recurrent Neural Networks (RNN). We train Long Short-Term Memory (LSTM) cells in several configurations and embedding levels to model the various morphological features. Our experiments show that these models outperform state-of-the-art systems without explicit use of feature engineering. However, adding learning features from a morphological analyzer to model the space of possible analyses provides additional improvement. We make use of the resulting morphological models for scoring and ranking the analyses of the morphological analyzer for morphological disambiguation. The results show significant gains in accuracy across several evaluation metrics. Our system results in 4.4{\%} absolute increase over the state-of-the-art in full morphological analysis accuracy (30.6{\%} relative error reduction), and 10.6{\%} (31.5{\%} relative error reduction) for out-of-vocabulary words.
Do LSTMs really work so well for PoS tagging? -- A replication study
Authors: Tobias Horsmann, Torsten Zesch
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Feature Engineering, Part-Of-Speech Tagging, POS, POS Tagging

A recent study by Plank et al. (2016) found that LSTM-based PoS taggers considerably improve over the current state-of-the-art when evaluated on the corpora of the Universal Dependencies project that use a coarse-grained tagset. We replicate this study using a fresh collection of 27 corpora of 21 languages that are annotated with fine-grained tagsets of varying size. Our replication confirms the result in general, and we additionally find that the advantage of LSTMs is even bigger for larger tagsets. However, we also find that for the very large tagsets of morphologically rich languages, hand-crafted morphological lexicons are still necessary to reach state-of-the-art performance.
The Labeled Segmentation of Printed Books
Authors: Lara McConnaughey, Jennifer Dai, David Bamman
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Optical Character Recognition (OCR), Segmentation

We introduce the task of book structure labeling: segmenting and assigning a fixed category (such as Table of Contents, Preface, Index) to the document structure of printed books. We manually annotate the page-level structural categories for a large dataset totaling 294,816 pages in 1,055 books evenly sampled from 1750-1922, and present empirical results comparing the performance of several classes of models. The best-performing model, a bidirectional LSTM with rich features, achieves an overall accuracy of 95.8 and a class-balanced macro F-score of 71.4.
Cross-lingual Character-Level Neural Morphological Tagging
Authors: Ryan Cotterell, Georg Heigold
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Language Modeling, Language Modelling, Morphological Tagging, Part-Of-Speech Tagging, Transfer Learning

Even for common NLP tasks, sufficient supervision is not available in many languages {--} morphological tagging is no exception. In the work presented here, we explore a transfer learning scheme, whereby we train character-level recurrent neural taggers to predict morphological taggings for high-resource languages and low-resource languages together. Learning joint character representations among multiple related languages successfully enables knowledge transfer from the high-resource languages to the low-resource ones.
Word-Context Character Embeddings for Chinese Word Segmentation
Authors: Hao Zhou, Zhenting Yu, Yue Zhang, Shu-Jian Huang, Xin-yu Dai, Jia-Jun Chen
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Chinese Word Segmentation, Domain Adaptation, Segmentation, Word Embeddings

Neural parsers have benefited from automatically labeled data via dependency-context word embeddings. We investigate training character embeddings on a word-based context in a similar way, showing that the simple method improves state-of-the-art neural word segmentation models significantly, beating tri-training baselines for leveraging auto-segmented data.
Segmentation-Free Word Embedding for Unsegmented Languages
Authors: Takamasa Oshikiri
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Machine Translation, Segmentation

In this paper, we propose a new pipeline of word embedding for unsegmented languages, called segmentation-free word embedding, which does not require word segmentation as a preprocessing step. Unlike space-delimited languages, unsegmented languages, such as Chinese and Japanese, require word segmentation as a preprocessing step. However, word segmentation, that often requires manually annotated resources, is difficult and expensive, and unavoidable errors in word segmentation affect downstream tasks. To avoid these problems in learning word vectors of unsegmented languages, we consider word co-occurrence statistics over all possible candidates of segmentations based on frequent character n-grams instead of segmented sentences provided by conventional word segmenters. Our experiments of noun category prediction tasks on raw Twitter, Weibo, and Wikipedia corpora show that the proposed method outperforms the conventional approaches that require word segmenters.
From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems
Authors: Mrinmaya Sachan, Kumar Dubey, Eric Xing
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Math, Question Answering

Textbooks are rich sources of information. Harvesting structured knowledge from textbooks is a key challenge in many educational applications. As a case study, we present an approach for harvesting structured axiomatic knowledge from math textbooks. Our approach uses rich contextual and typographical features extracted from raw textbooks. It leverages the redundancy and shared ordering across multiple textbooks to further refine the harvested axioms. These axioms are then parsed into rules that are used to improve the state-of-the-art in solving geometry problems.
Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers
Authors: Mark Hopkins, Cristian Petrescu-Prahova, Roie Levin, Ronan Le Bras, Alvaro Herrasti, Vidur Joshi
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: coreference-resolution, Coreference Resolution, Math, Question Answering, Semantic Parsing, Sentence

We present an approach for answering questions that span multiple sentences and exhibit sophisticated cross-sentence anaphoric phenomena, evaluating on a rich source of such questions {--} the math portion of the Scholastic Aptitude Test (SAT). By using a tree transducer cascade as its basic architecture, our system propagates uncertainty from multiple sources (e.g. coreference resolution or verb interpretation) until it can be confidently resolved. Experiments show the first-ever results 43{\%} recall and 91{\%} precision) on SAT algebra word problems. We also apply our system to the public Dolphin algebra question set, and improve the state-of-the-art F1-score from 73.9{\%} to 77.0{\%}.
Learning Fine-Grained Expressions to Solve Math Word Problems
Authors: Danqing Huang, Shuming Shi, Chin-Yew Lin, Jian Yin
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Math, Math Word Problem Solving

This paper presents a novel template-based method to solve math word problems. This method learns the mappings between math concept phrases in math word problems and their math expressions from training data. For each equation template, we automatically construct a rich template sketch by aggregating information from various problems with the same template. Our approach is implemented in a two-stage system. It first retrieves a few relevant equation system templates and aligns numbers in math word problems to those templates for candidate equation generation. It then does a fine-grained inference to obtain the final answer. Experiment results show that our method achieves an accuracy of 28.4{\%} on the linear Dolphin18K benchmark, which is 10{\%} (54{\%} relative) higher than previous state-of-the-art systems while achieving an accuracy increase of 12{\%} (59{\%} relative) on the TS6 benchmark subset.
World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions
Authors: Teng Long, Emmanuel Bengio, Ryan Lowe, Jackie Chi Kit Cheung, Doina Precup
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Diversity, Language Modeling, Language Modelling, Reading Comprehension, World Knowledge

Humans interpret texts with respect to some background information, or world knowledge, and we would like to develop automatic reading comprehension systems that can do the same. In this paper, we introduce a task and several models to drive progress towards this goal. In particular, we propose the task of rare entity prediction: given a web document with several entities removed, models are tasked with predicting the correct missing entities conditioned on the document context and the lexical resources. This task is challenging due to the diversity of language styles and the extremely large number of rare entities. We propose two recurrent neural network architectures which make use of external knowledge in the form of entity descriptions. Our experiments show that our hierarchical LSTM model performs significantly better at the rare entity prediction task than those that do not make use of external resources.
Deep Neural Solver for Math Word Problems
Authors: Yan Wang, Xiaojiang Liu, Shuming Shi
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Feature Engineering, Machine Translation, Math, Math Word Problem Solving, Retrieval, Semantic Parsing

This paper presents a deep neural solver to automatically solve math word problems. In contrast to previous statistical learning approaches, we directly translate math word problems to equation templates using a recurrent neural network (RNN) model, without sophisticated feature engineering. We further design a hybrid model that combines the RNN model and a similarity-based retrieval model to achieve additional performance improvement. Experiments conducted on a large dataset show that the RNN model and the hybrid model significantly outperform state-of-the-art statistical learning methods for math word problem solving.
Latent Space Embedding for Retrieval in Question-Answer Archives
Authors: Deepak P, Dinesh Garg, Shirish Shevade
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Question Answering, Retrieval, Semantic Similarity, Semantic Textual Similarity, Topic Models, Translation

Community-driven Question Answering (CQA) systems such as Yahoo! Answers have become valuable sources of reusable information. CQA retrieval enables usage of historical CQA archives to solve new questions posed by users. This task has received much recent attention, with methods building upon literature from translation models, topic models, and deep learning. In this paper, we devise a CQA retrieval technique, LASER-QA, that embeds question-answer pairs within a unified latent space preserving the local neighborhood structure of question and answer spaces. The idea is that such a space mirrors semantic similarity among questions as well as answers, thereby enabling high quality retrieval. Through an empirical analysis on various real-world QA datasets, we illustrate the improved effectiveness of LASER-QA over state-of-the-art methods.
Question Generation for Question Answering
Authors: Nan Duan, Duyu Tang, Peng Chen, Ming Zhou
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Chatbot, Question Answering, Question Generation, Question-Generation, Reading Comprehension, Retrieval, Sentence

This paper presents how to generate questions from given passages using neural networks, where large scale QA pairs are automatically crawled and processed from Community-QA website, and used as training data. The contribution of the paper is 2-fold: First, two types of question generation approaches are proposed, one is a retrieval-based method using convolution neural network (CNN), the other is a generation-based method using recurrent neural network (RNN); Second, we show how to leverage the generated questions to improve existing question answering systems. We evaluate our question generation method for the answer sentence selection task on three benchmark datasets, including SQuAD, MS MARCO, and WikiQA. Experimental results show that, by using generated questions as an extra signal, significant QA improvement can be achieved.
Ranking Kernels for Structures and Embeddings: A Hybrid Preference and Classification Model
Authors: Kateryna Tymoshenko, Daniele Bonadiman, Aless Moschitti, ro
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Community Question Answering, General Classification, Learning-To-Rank, Question Answering, Reranking, Sentence, Word Embeddings

Recent work has shown that Tree Kernels (TKs) and Convolutional Neural Networks (CNNs) obtain the state of the art in answer sentence reranking. Additionally, their combination used in Support Vector Machines (SVMs) is promising as it can exploit both the syntactic patterns captured by TKs and the embeddings learned by CNNs. However, the embeddings are constructed according to a classification function, which is not directly exploitable in the preference ranking algorithm of SVMs. In this work, we propose a new hybrid approach combining preference ranking applied to TKs and pointwise ranking applied to CNNs. We show that our approach produces better results on two well-known and rather different datasets: WikiQA for answer sentence selection and SemEval cQA for comment selection in Community Question Answering.
Recovering Question Answering Errors via Query Revision
Authors: Semih Yavuz, Izzeddin Gur, Yu Su, Xifeng Yan
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Question Answering, Semantic Parsing

The existing factoid QA systems often lack a post-inspection component that can help models recover from their own mistakes. In this work, we propose to crosscheck the corresponding KB relations behind the predicted answers and identify potential inconsistencies. Instead of developing a new model that accepts evidences collected from these relations, we choose to plug them back to the original questions directly and check if the revised question makes sense or not. A bidirectional LSTM is applied to encode revised questions. We develop a scoring mechanism over the revised question encodings to refine the predictions of a base QA system. This approach can improve the F1 score of STAGG (Yih et al., 2015), one of the leading QA systems, from 52.5{\%} to 53.9{\%} on WEBQUESTIONS data.
Deriving continous grounded meaning representations from referentially structured multimodal contexts
Authors: Sina Zarrie{\ss}, David Schlangen
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Attribute, Word Embeddings

Corpora of referring expressions paired with their visual referents are a good source for learning word meanings directly grounded in visual representations. Here, we explore additional ways of extracting from them word representations linked to multi-modal context: through expressions that refer to the same object, and through expressions that refer to different objects in the same scene. We show that continuous meaning representations derived from these contexts capture complementary aspects of similarity, , even if not outperforming textual embeddings trained on very large amounts of raw text when tested on standard similarity benchmarks. We propose a new task for evaluating grounded meaning representations{---}detection of potentially co-referential phrases{---}and show that it requires precise denotational representations of attribute meanings, which our method provides.
Evaluating Hierarchies of Verb Argument Structure with Hierarchical Clustering
Authors: Jesse Mu, Joshua K. Hartshorne, Timothy O{'}Donnell
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Clustering, Language Acquisition, Natural Language Inference, Semantic Parsing

Verbs can only be used with a few specific arrangements of their arguments (syntactic frames). Most theorists note that verbs can be organized into a hierarchy of verb classes based on the frames they admit. Here we show that such a hierarchy is objectively well-supported by the patterns of verbs and frames in English, since a systematic hierarchical clustering algorithm converges on the same structure as the handcrafted taxonomy of VerbNet, a broad-coverage verb lexicon. We also show that the hierarchies capture meaningful psychological dimensions of generalization by predicting novel verb coercions by human participants. We discuss limitations of a simple hierarchical representation and suggest similar approaches for identifying the representations underpinning verb argument structure.
Incorporating Global Visual Features into Attention-based Neural Machine Translation.
Authors: Iacer Calixto, Qun Liu
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Decoder, Machine Translation, NMT, Sentence, Text Generation, Translation, Video Description

We introduce multi-modal, attention-based neural machine translation (NMT) models which incorporate visual features into different parts of both the encoder and the decoder. Global image features are extracted using a pre-trained convolutional neural network and are incorporated (i) as words in the source sentence, (ii) to initialise the encoder hidden state, and (iii) as additional data to initialise the decoder hidden state. In our experiments, we evaluate translations into English and German, how different strategies to incorporate global image features compare and which ones perform best. We also study the impact that adding synthetic multi-modal, multilingual data brings and find that the additional data have a positive impact on multi-modal NMT models. We report new state-of-the-art results and our best models also significantly improve on a comparable phrase-based Statistical MT (PBSMT) model trained on the Multi30k data set according to all metrics evaluated. To the best of our knowledge, it is the first time a purely neural model significantly improves over a PBSMT model on all metrics evaluated on this data set.
An analysis of eye-movements during reading for the detection of mild cognitive impairment
Authors: Kathleen C. Fraser, Kristina Lundholm Fors, Dimitrios Kokkinakis, Arto Nordlund
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: BIG-bench Machine Learning

We present a machine learning analysis of eye-tracking data for the detection of mild cognitive impairment, a decline in cognitive abilities that is associated with an increased risk of developing dementia. We compare two experimental configurations (reading aloud versus reading silently), as well as two methods of combining information from the two trials (concatenation and merging). Additionally, we annotate the words being read with information about their frequency and syntactic category, and use these annotations to generate new features. Ultimately, we are able to distinguish between participants with and without cognitive impairment with up to 86{\%} accuracy.