pwc-archive/papers-with-abstracts huggingface dataset:
Importance sampling for unbiased on-demand evaluation of knowledge base population
Authors: Arun Chaganty, Ashwin Paranjape, Percy Liang, Christopher D. Manning
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Information Retrieval, Knowledge Base Population, Question Answering

Knowledge base population (KBP) systems take in a large document corpus and extract entities and their relations. Thus far, KBP evaluation has relied on judgements on the pooled predictions of existing systems. We show that this evaluation is problematic: when a new system predicts a previously unseen relation, it is penalized even if it is correct. This leads to significant bias against new systems, which counterproductively discourages innovation in the field. Our first contribution is a new importance-sampling based evaluation which corrects for this bias by annotating a new system{'}s predictions on-demand via crowdsourcing. We show this eliminates bias and reduces variance using data from the 2015 TAC KBP task. Our second contribution is an implementation of our method made publicly available as an online KBP evaluation service. We pilot the service by testing diverse state-of-the-art systems on the TAC KBP 2016 corpus and obtain accurate scores in a cost effective manner.
Speech segmentation with a neural encoder model of working memory
Authors: Micha Elsner, Cory Shain
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Decoder

We present the first unsupervised LSTM speech segmenter as a cognitive model of the acquisition of words from unsegmented input. Cognitive biases toward phonological and syntactic predictability in speech are rooted in the limitations of human memory (Baddeley et al., 1998); compressed representations are easier to acquire and retain in memory. To model the biases introduced by these memory limitations, our system uses an LSTM-based encoder-decoder with a small number of hidden units, then searches for a segmentation that minimizes autoencoding loss. Linguistically meaningful segments (e.g. words) should share regular patterns of features that facilitate decoder performance in comparison to random segmentations, and we show that our learner discovers these patterns when trained on either phoneme sequences or raw acoustics. To our knowledge, ours is the first fully unsupervised system to be able to segment both symbolic and acoustic representations of speech.
Speaking, Seeing, Understanding: Correlating semantic models with conceptual representation in the brain
Authors: Luana Bulat, Stephen Clark, Ekaterina Shutova
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Semantic Textual Similarity

Research in computational semantics is increasingly guided by our understanding of human semantic processing. However, semantic models are typically studied in the context of natural language processing system performance. In this paper, we present a systematic evaluation and comparison of a range of widely-used, state-of-the-art semantic models in their ability to predict patterns of conceptual representation in the human brain. Our results provide new insights both for the design of computational semantic models and for further research in cognitive neuroscience.
Multi-modal Summarization for Asynchronous Collection of Text, Image, Audio and Video
Authors: Haoran Li, Junnan Zhu, Cong Ma, Jiajun Zhang, Cheng-qing Zong
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Automatic Speech Recognition (ASR), Document Summarization, Speech Recognition, Video Summarization

The rapid increase of the multimedia data over the Internet necessitates multi-modal summarization from collections of text, image, audio and video. In this work, we propose an extractive Multi-modal Summarization (MMS) method which can automatically generate a textual summary given a set of documents, images, audios and videos related to a specific topic. The key idea is to bridge the semantic gaps between multi-modal contents. For audio information, we design an approach to selectively use its transcription. For vision information, we learn joint representations of texts and images using a neural network. Finally, all the multi-modal aspects are considered to generate the textural summary by maximizing the salience, non-redundancy, readability and coverage through budgeted optimization of submodular functions. We further introduce an MMS corpus in English and Chinese. The experimental results on this dataset demonstrate that our method outperforms other competitive baseline methods.
Deeper Attention to Abusive User Content Moderation
Authors: John Pavlopoulos, Prodromos Malakasiotis, Ion Androutsopoulos
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: regression, Word Embeddings

Experimenting with a new dataset of 1.6M user comments from a news portal and an existing dataset of 115K Wikipedia talk page comments, we show that an RNN operating on word embeddings outpeforms the previous state of the art in moderation, which used logistic regression or an MLP classifier with character or word n-grams. We also compare against a CNN operating on word embeddings, and a word-list baseline. A novel, deep, classificationspecific attention mechanism improves the performance of the RNN further, and can also highlight suspicious words for free, without including highlighted words in the training data. We consider both fully automatic and semi-automatic moderation.
Outta Control: Laws of Semantic Change and Inherent Biases in Word Representation Models
Authors: Haim Dubossarsky, Daphna Weinshall, Eitan Grossman
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9

This article evaluates three proposed laws of semantic change. Our claim is that in order to validate a putative law of semantic change, the effect should be observed in the genuine condition but absent or reduced in a suitably matched control condition, in which no change can possibly have taken place. Our analysis shows that the effects reported in recent literature must be substantially revised: (i) the proposed negative correlation between meaning change and word frequency is shown to be largely an artefact of the models of word representation used; (ii) the proposed negative correlation between meaning change and prototypicality is shown to be much weaker than what has been claimed in prior art; and (iii) the proposed positive correlation between meaning change and polysemy is largely an artefact of word frequency. These empirical observations are corroborated by analytical proofs that show that count representations introduce an inherent dependence on word frequency, and thus word frequency cannot be evaluated as an independent factor with these representations.
Human Centered NLP with User-Factor Adaptation
Authors: Veronica Lynn, Youngseo Son, Vivek Kulkarni, Niranjan Balasubramanian, H. Andrew Schwartz
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Document Classification, Domain Adaptation, POS, POS Tagging, Sarcasm Detection, Sentiment Analysis, Stance Detection

We pose the general task of user-factor adaptation {--} adapting supervised learning models to real-valued user factors inferred from a background of their language, reflecting the idea that a piece of text should be understood within the context of the user that wrote it. We introduce a continuous adaptation technique, suited for real-valued user factors that are common in social science and bringing us closer to personalized NLP, adapting to each user uniquely. We apply this technique with known user factors including age, gender, and personality traits, as well as latent factors, evaluating over five tasks: POS tagging, PP-attachment, sentiment analysis, sarcasm detection, and stance detection. Adaptation provides statistically significant benefits for 3 of the 5 tasks: up to +1.2 points for PP-attachment, +3.4 points for sarcasm, and +3.0 points for stance.
Neural Sequence Learning Models for Word Sense Disambiguation
Authors: Aless Raganato, ro, Claudio Delli Bovi, Roberto Navigli
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Decoder, Information Retrieval, Machine Translation, Word Sense Disambiguation

Word Sense Disambiguation models exist in many flavors. Even though supervised ones tend to perform best in terms of accuracy, they often lose ground to more flexible knowledge-based solutions, which do not require training by a word expert for every disambiguation target. To bridge this gap we adopt a different perspective and rely on sequence learning to frame the disambiguation problem: we propose and study in depth a series of end-to-end neural architectures directly tailored to the task, from bidirectional Long Short-Term Memory to encoder-decoder models. Our extensive evaluation over standard benchmarks and in multiple languages shows that sequence learning enables more versatile all-words models that consistently lead to state-of-the-art results, even against word experts with engineered features.
Inter-Weighted Alignment Network for Sentence Pair Modeling
Authors: Gehui Shen, Yunlun Yang, Zhi-Hong Deng
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Machine Translation, Natural Language Inference, Paraphrase Identification, Question Answering, Relation Classification, Semantic Composition, Semantic Textual Similarity, Sentence, Sentence Pair Modeling, Word Embeddings

Sentence pair modeling is a crucial problem in the field of natural language processing. In this paper, we propose a model to measure the similarity of a sentence pair focusing on the interaction information. We utilize the word level similarity matrix to discover fine-grained alignment of two sentences. It should be emphasized that each word in a sentence has a different importance from the perspective of semantic composition, so we exploit two novel and efficient strategies to explicitly calculate a weight for each word. Although the proposed model only use a sequential LSTM for sentence modeling without any external resource such as syntactic parser tree and additional lexicon features, experimental results show that our model achieves state-of-the-art performance on three datasets of two tasks.
A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances
Authors: Chengyu Wang, Xiaofeng He, Aoying Zhou
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Question Answering

A taxonomy is a semantic hierarchy, consisting of concepts linked by is-a relations. While a large number of taxonomies have been constructed from human-compiled resources (e.g., Wikipedia), learning taxonomies from text corpora has received a growing interest and is essential for long-tailed and domain-specific knowledge acquisition. In this paper, we overview recent advances on taxonomy construction from free texts, reorganizing relevant subtasks into a complete framework. We also overview resources for evaluation and discuss challenges for future research.
Idiom-Aware Compositional Distributed Semantics
Authors: Pengfei Liu, Kaiyu Qian, Xipeng Qiu, Xuanjing Huang
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: General Classification, Machine Translation, Sentence, Sentiment Analysis, Sentiment Classification, Text Classification

Idioms are peculiar linguistic constructions that impose great challenges for representing the semantics of language, especially in current prevailing end-to-end neural models, which assume that the semantics of a phrase or sentence can be literally composed from its constitutive words. In this paper, we propose an idiom-aware distributed semantic model to build representation of sentences on the basis of understanding their contained idioms. Our models are grounded in the literal-first psycholinguistic hypothesis, which can adaptively learn semantic compositionality of a phrase literally or idiomatically. To better evaluate our models, we also construct an idiom-enriched sentiment classification dataset with considerable scale and abundant peculiarities of idioms. The qualitative and quantitative experimental analyses demonstrate the efficacy of our models.
A Joint Sequential and Relational Model for Frame-Semantic Parsing
Authors: Bishan Yang, Tom Mitchell
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Knowledge Distillation, Machine Translation, Question Answering, Semantic Parsing, Semantic Role Labeling, Structured Prediction, Word Sense Disambiguation

We introduce a new method for frame-semantic parsing that significantly improves the prior state of the art. Our model leverages the advantages of a deep bidirectional LSTM network which predicts semantic role labels word by word and a relational network which predicts semantic roles for individual text expressions in relation to a predicate. The two networks are integrated into a single model via knowledge distillation, and a unified graphical model is employed to jointly decode frames and semantic roles during inference. Experiments on the standard FrameNet data show that our model significantly outperforms existing neural and non-neural approaches, achieving a 5.7 F1 gain over the current state of the art, for full frame structure extraction.
Getting the Most out of AMR Parsing
Authors: Chuan Wang, Nianwen Xue
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: AMR Parsing, Concept Alignment, Feature Engineering, Reading Comprehension, Text Generation

This paper proposes to tackle the AMR parsing bottleneck by improving two components of an AMR parser: concept identification and alignment. We first build a Bidirectional LSTM based concept identifier that is able to incorporate richer contextual information to learn sparse AMR concept labels. We then extend an HMM-based word-to-concept alignment model with graph distance distortion and a rescoring method during decoding to incorporate the structural information in the AMR graph. We show integrating the two components into an existing AMR parser results in consistently better performance over the state of the art on various datasets.
An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective
Authors: Jie Zhao, Yu Su, Ziyu Guan, Huan Sun
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Multiple Instance Learning, Question Answering

Given a question and a set of answer candidates, answer triggering determines whether the candidate set contains any correct answers. If yes, it then outputs a correct one. In contrast to existing pipeline methods which first consider individual candidate answers separately and then make a prediction based on a threshold, we propose an end-to-end deep neural network framework, which is trained by a novel group-level objective function that directly optimizes the answer triggering performance. Our objective function penalizes three potential types of error and allows training the framework in an end-to-end manner. Experimental results on the WikiQA benchmark show that our framework outperforms the state of the arts by a 6.6{\%} absolute gain under F1 measure.
Predicting Word Association Strengths
Authors: Andrew Cattle, Xiaojuan Ma
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Word Embeddings

This paper looks at the task of predicting word association strengths across three datasets; WordNet Evocation (Boyd-Graber et al., 2006), University of Southern Florida Free Association norms (Nelson et al., 2004), and Edinburgh Associative Thesaurus (Kiss et al., 1973). We achieve results of r=0.357 and p=0.379, r=0.344 and p=0.300, and r=0.292 and p=0.363, respectively. We find Word2Vec (Mikolov et al., 2013) and GloVe (Pennington et al., 2014) cosine similarities, as well as vector offsets, to be the highest performing features. Furthermore, we examine the usefulness of Gaussian embeddings (Vilnis and McCallum, 2014) for predicting word association strength, the first work to do so.
Learning Contextually Informed Representations for Linear-Time Discourse Parsing
Authors: Yang Liu, Mirella Lapata
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Discourse Parsing, Feature Engineering, Question Answering, Sentiment Analysis

Recent advances in RST discourse parsing have focused on two modeling paradigms: (a) high order parsers which jointly predict the tree structure of the discourse and the relations it encodes; or (b) linear-time parsers which are efficient but mostly based on local features. In this work, we propose a linear-time parser with a novel way of representing discourse constituents based on neural networks which takes into account global contextual information and is able to capture long-distance dependencies. Experimental results show that our parser obtains state-of-the art performance on benchmark datasets, while being efficient (with time complexity linear in the number of sentences in the document) and requiring minimal feature engineering.
Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification
Authors: Man Lan, Jianxiang Wang, Yuanbin Wu, Zheng-Yu Niu, Haifeng Wang
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Multi-Task Learning, Reading Comprehension, Representation Learning, Sentiment Analysis, Text Generation

We present a novel multi-task attention based neural network model to address implicit discourse relationship representation and identification through two types of representation learning, an attention based neural network for learning discourse relationship representation with two arguments and a multi-task framework for learning knowledge from annotated and unannotated corpora. The extensive experiments have been performed on two benchmark corpora (i.e., PDTB and CoNLL-2016 datasets). Experimental results show that our proposed model outperforms the state-of-the-art systems on benchmark corpora.
Chinese Zero Pronoun Resolution with Deep Memory Network
Authors: Qingyu Yin, Yu Zhang, Wei-Nan Zhang, Ting Liu
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Chinese Zero Pronoun Resolution, Descriptive, Feature Engineering, Information Retrieval

Existing approaches for Chinese zero pronoun resolution typically utilize only syntactical and lexical features while ignoring semantic information. The fundamental reason is that zero pronouns have no descriptive information, which brings difficulty in explicitly capturing their semantic similarities with antecedents. Meanwhile, representing zero pronouns is challenging since they are merely gaps that convey no actual content. In this paper, we address this issue by building a deep memory network that is capable of encoding zero pronouns into vector representations with information obtained from their contexts and potential antecedents. Consequently, our resolver takes advantage of semantic information by using these continuous distributed representations. Experiments on the OntoNotes 5.0 dataset show that the proposed memory network could substantially outperform the state-of-the-art systems in various experimental settings.
How much progress have we made on RST discourse parsing? A replication study of recent results on the RST-DT
Authors: Mathieu Morey, Philippe Muller, Nicholas Asher
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Attribute, Discourse Parsing

This article evaluates purported progress over the past years in RST discourse parsing. Several studies report a relative error reduction of 24 to 51{\%} on all metrics that authors attribute to the introduction of distributed representations of discourse units. We replicate the standard evaluation of 9 parsers, 5 of which use distributed representations, from 8 studies published between 2013 and 2017, using their predictions on the test set of the RST-DT. Our main finding is that most recently reported increases in RST discourse parser performance are an artefact of differences in implementations of the evaluation procedure. We evaluate all these parsers with the standard Parseval procedure to provide a more accurate picture of the actual RST discourse parsers performance in standard evaluation settings. Under this more stringent procedure, the gains attributable to distributed representations represent at most a 16{\%} relative error reduction on fully-labelled structures.
What is it? Disambiguating the different readings of the pronoun `it'
Authors: Sharid Lo{\'a}iciga, Liane Guillou, Christian Hardmeier
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: coreference-resolution, Coreference Resolution, Machine Translation, Translation

In this paper, we address the problem of predicting one of three functions for the English pronoun {`}it{'}: anaphoric, event reference or pleonastic. This disambiguation is valuable in the context of machine translation and coreference resolution. We present experiments using a MAXENT classifier trained on gold-standard data and self-training experiments of an RNN trained on silver-standard data, annotated using the MAXENT classifier. Lastly, we report on an analysis of the strengths of these two models.
Learning to Rank Semantic Coherence for Topic Segmentation
Authors: Liang Wang, Sujian Li, Yajuan Lv, Houfeng Wang
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Common Sense Reasoning, Discourse Parsing, Information Retrieval, Learning-To-Rank, Retrieval, Segmentation, Topic Models

Topic segmentation plays an important role for discourse parsing and information retrieval. Due to the absence of training data, previous work mainly adopts unsupervised methods to rank semantic coherence between paragraphs for topic segmentation. In this paper, we present an intuitive and simple idea to automatically create a {``}quasi{''} training dataset, which includes a large amount of text pairs from the same or different documents with different semantic coherence. With the training corpus, we design a symmetric CNN neural network to model text pairs and rank the semantic coherence within the learning to rank framework. Experiments show that our algorithm is able to achieve competitive performance over strong baselines on several real-world datasets.
GRASP: Rich Patterns for Argumentation Mining
Authors: Eyal Shnarch, Ran Levy, Vikas Raykar, Noam Slonim
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Document Classification

GRASP (GReedy Augmented Sequential Patterns) is an algorithm for automatically extracting patterns that characterize subtle linguistic phenomena. To that end, GRASP augments each term of input text with multiple layers of linguistic information. These different facets of the text terms are systematically combined to reveal rich patterns. We report highly promising experimental results in several challenging text analysis tasks within the field of Argumentation Mining. We believe that GRASP is general enough to be useful for other domains too. For example, each of the following sentences includes a claim for a [topic]: 1. Opponents often argue that the open primary is unconstitutional. [Open Primaries] 2. Prof. Smith suggested that affirmative action devalues the accomplishments of the chosen. [Affirmative Action] 3. The majority stated that the First Amendment does not guarantee the right to offend others. [Freedom of Speech] These sentences share almost no words in common, however, they are similar at a more abstract level. A human observer may notice the following underlying common structure, or pattern: [someone][argue/suggest/state][that][topic term][sentiment term]. GRASP aims to automatically capture such underlying structures of the given data. For the above examples it finds the pattern [noun][express][that][noun,topic][sentiment], where [express] stands for all its (in)direct hyponyms, and [noun,topic] means a noun which is also related to the topic.