pwc-archive/papers-with-abstracts huggingface dataset:
Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse
Authors: Xingshan Zeng, Jing Li, Lu Wang, Nicholas Beauchamp, Sarah Shugars, Kam-Fai Wong
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6

Millions of conversations are generated every day on social media platforms. With limited attention, it is challenging for users to select which discussions they would like to participate in. Here we propose a new method for microblog conversation recommendation. While much prior work has focused on post-level recommendation, we exploit both the conversational context, and user content and behavior preferences. We propose a statistical model that jointly captures: (1) topics for representing user interests and conversation content, and (2) discourse modes for describing user replying behavior and conversation dynamics. Experimental results on two Twitter datasets demonstrate that our system outperforms methods that only model content without considering discourse.
Abstract Meaning Representation for Paraphrase Detection
Authors: Fuad Issa, Marco Damonte, Shay B. Cohen, Xiaohui Yan, Yi Chang
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Abstract Meaning Representation, AMR Parsing, Sentence

Abstract Meaning Representation (AMR) parsing aims at abstracting away from the syntactic realization of a sentence, and denote only its meaning in a canonical form. As such, it is ideal for paraphrase detection, a problem in which one is required to specify whether two sentences have the same meaning. We show that na{\"\i}ve use of AMR in paraphrase detection is not necessarily useful, and turn to describe a technique based on latent semantic analysis in combination with AMR parsing that significantly advances state-of-the-art results in paraphrase detection for the Microsoft Research Paraphrase Corpus. Our best results in the transductive setting are 86.6{\%} for accuracy and 90.0{\%} for F$_1$ measure.
attr2vec: Jointly Learning Word and Contextual Attribute Embeddings with Factorization Machines
Authors: Fabio Petroni, Vassilis Plachouras, Timothy Nugent, Jochen L. Leidner
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Attribute, Dependency Parsing, Information Retrieval, Machine Translation, POS, Sentence Classification, text-classification, Text Classification, Word Embeddings

The widespread use of word embeddings is associated with the recent successes of many natural language processing (NLP) systems. The key approach of popular models such as word2vec and GloVe is to learn dense vector representations from the context of words. More recently, other approaches have been proposed that incorporate different types of contextual information, including topics, dependency relations, n-grams, and sentiment. However, these models typically integrate only limited additional contextual information, and often in ad hoc ways. In this work, we introduce attr2vec, a novel framework for jointly learning embeddings for words and contextual attributes based on factorization machines. We perform experiments with different types of contextual information. Our experimental results on a text classification task demonstrate that using attr2vec to jointly learn embeddings for words and Part-of-Speech (POS) tags improves results compared to learning the embeddings independently. Moreover, we use attr2vec to train dependency-based embeddings and we show that they exhibit higher similarity between functionally related words compared to traditional approaches.
Deep Neural Models of Semantic Shift
Authors: Alex Rosenfeld, Katrin Erk
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Time Series, Time Series Analysis

Diachronic distributional models track changes in word use over time. In this paper, we propose a deep neural network diachronic distributional model. Instead of modeling lexical change via a time series as is done in previous work, we represent time as a continuous variable and model a word{'}s usage as a function of time. Additionally, we have also created a novel synthetic task which measures a model{'}s ability to capture the semantic trajectory. This evaluation quantitatively measures how well a model captures the semantic trajectory of a word over time. Finally, we explore how well the derivatives of our model can be used to measure the speed of lexical change.
Mining Possessions: Existence, Type and Temporal Anchors
Authors: Dhivya Chinnappa, Eduardo Blanco
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Vocal Bursts Type Prediction

This paper presents a corpus and experiments to mine possession relations from text. Specifically, we target alienable and control possessions, and assign temporal anchors indicating when the possession holds between possessor and possessee. We present new annotations for this task, and experimental results using both traditional classifiers and neural networks. Results show that the three subtasks (predicting possession existence, possession type and temporal anchors) can be automated.
Neural Tensor Networks with Diagonal Slice Matrices
Authors: Takahiro Ishihara, Katsuhiko Hayashi, Hitoshi Manabe, Masashi Shimbo, Masaaki Nagata
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Knowledge Graph Completion, Logical Reasoning, Sentiment Analysis, Tensor Networks

Although neural tensor networks (NTNs) have been successful in many NLP tasks, they require a large number of parameters to be estimated, which often leads to overfitting and a long training time. We address these issues by applying eigendecomposition to each slice matrix of a tensor to reduce its number of paramters. First, we evaluate our proposed NTN models on knowledge graph completion. Second, we extend the models to recursive NTNs (RNTNs) and evaluate them on logical reasoning tasks. These experiments show that our proposed models learn better and faster than the original (R)NTNs.
Learning Domain Representation for Multi-Domain Sentiment Classification
Authors: Qi Liu, Yue Zhang, Jiangming Liu
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Classification, General Classification, Multi-Domain Sentiment Classification, Multi-Task Learning, Sentiment Analysis, Sentiment Classification

Training data for sentiment analysis are abundant in multiple domains, yet scarce for other domains. It is useful to leveraging data available for all existing domains to enhance performance on different domains. We investigate this problem by learning domain-specific representations of input sentences using neural network. In particular, a descriptor vector is learned for representing each domain, which is used to map adversarially trained domain-general Bi-LSTM input representations into domain-specific representations. Based on this model, we further expand the input representation with exemplary domain knowledge, collected by attending over a memory network of domain training data. Results show that our model outperforms existing methods on multi-domain sentiment analysis significantly, giving the best accuracies on two different benchmarks.
Learning Sentence Representations over Tree Structures for Target-Dependent Classification
Authors: Junwen Duan, Xiao Ding, Ting Liu
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Classification, Decoder, General Classification, Information Retrieval, Reinforcement Learning, Sentence, Sentiment Analysis, Stance Detection

Target-dependent classification tasks, such as aspect-level sentiment analysis, perform fine-grained classifications towards specific targets. Semantic compositions over tree structures are promising for such tasks, as they can potentially capture long-distance interactions between targets and their contexts. However, previous work that operates on tree structures resorts to syntactic parsers or Treebank annotations, which are either subject to noise in informal texts or highly expensive to obtain. To address above issues, we propose a reinforcement learning based approach, which automatically induces target-specific sentence representations over tree structures. The underlying model is a RNN encoder-decoder that explores possible binary tree structures and a reward mechanism that encourages structures that improve performances on downstream tasks. We evaluate our approach on two benchmark tasks: firm-specific cumulative abnormal return prediction (based on formal news texts) and aspect-level sentiment analysis (based on informal social media texts). Experimental results show that our model gives superior performances compared to previous work that operates on parsed trees. Moreover, our approach gives some intuitions on how target-specific sentence representations can be achieved from its word constituents.
Relevant Emotion Ranking from Text Constrained with Emotion Relationships
Authors: Deyu Zhou, Yang Yang, Yulan He
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Learning, Topic Models

Text might contain or invoke multiple emotions with varying intensities. As such, emotion detection, to predict multiple emotions associated with a given text, can be cast into a multi-label classification problem. We would like to go one step further so that a ranked list of relevant emotions are generated where top ranked emotions are more intensely associated with text compared to lower ranked emotions, whereas the rankings of irrelevant emotions are not important. A novel framework of relevant emotion ranking is proposed to tackle the problem. In the framework, the objective loss function is designed elaborately so that both emotion prediction and rankings of only relevant emotions can be achieved. Moreover, we observe that some emotions co-occur more often while other emotions rarely co-exist. Such information is incorporated into the framework as constraints to improve the accuracy of emotion detection. Experimental results on two real-world corpora show that the proposed framework can effectively deal with emotion detection and performs remarkably better than the state-of-the-art emotion detection approaches and multi-label learning methods.
Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality
Authors: Md. Shad Akhtar, Palaash Sawant, Sukanta Sen, Asif Ekbal, Pushpak Bhattacharyya
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Sentiment Classification, Word Embeddings

Efficient word representations play an important role in solving various problems related to Natural Language Processing (NLP), data mining, text mining etc. The issue of data sparsity poses a great challenge in creating efficient word representation model for solving the underlying problem. The problem is more intensified in resource-poor scenario due to the absence of sufficient amount of corpus. In this work we propose to minimize the effect of data sparsity by leveraging bilingual word embeddings learned through a parallel corpus. We train and evaluate Long Short Term Memory (LSTM) based architecture for aspect level sentiment classification. The neural network architecture is further assisted by the hand-crafted features for the prediction. We show the efficacy of the proposed model against state-of-the-art methods in two experimental setups i.e. multi-lingual and cross-lingual.
Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction
Authors: Ziang Xie, Guillaume Genthial, Stanley Xie, Andrew Ng, Dan Jurafsky
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Denoising, Machine Translation, Sentence, Translation

Translation-based methods for grammar correction that directly map noisy, ungrammatical text to their clean counterparts are able to correct a broad range of errors; however, such techniques are bottlenecked by the need for a large parallel corpus of noisy and clean sentence pairs. In this paper, we consider synthesizing parallel data by noising a clean monolingual corpus. While most previous approaches introduce perturbations using features computed from local context windows, we instead develop error generation processes using a neural sequence transduction model trained to translate clean examples to their noisy counterparts. Given a corpus of clean examples, we propose beam search noising procedures to synthesize additional noisy examples that human evaluators were nearly unable to discriminate from nonsynthesized examples. Surprisingly, when trained on additional data synthesized using our best-performing noising scheme, our model approaches the same performance as when trained on additional nonsynthesized data.
Self-Training for Jointly Learning to Ask and Answer Questions
Authors: Mrinmaya Sachan, Eric Xing
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Data Augmentation, Question Answering, Question Generation, Question-Generation, Sentence

Building curious machines that can answer as well as ask questions is an important challenge for AI. The two tasks of question answering and question generation are usually tackled separately in the NLP literature. At the same time, both require significant amounts of supervised data which is hard to obtain in many domains. To alleviate these issues, we propose a self-training method for jointly learning to ask as well as answer questions, leveraging unlabeled text along with labeled question answer pairs for learning. We evaluate our approach on four benchmark datasets: SQUAD, MS MARCO, WikiQA and TrecQA, and show significant improvements over a number of established baselines on both question answering and question generation tasks. We also achieved new state-of-the-art results on two competitive answer sentence selection tasks: WikiQA and TrecQA.
Fine-Grained Temporal Orientation and its Relationship with Psycho-Demographic Correlates
Authors: Sabyasachi Kamila, Mohammed Hasanuzzaman, Asif Ekbal, Pushpak Bhattacharyya, Andy Way
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Decision Making

Temporal orientation refers to an individual{'}s tendency to connect to the psychological concepts of past, present or future, and it affects personality, motivation, emotion, decision making and stress coping processes. The study of the social media users{'} psycho-demographic attributes from the perspective of human temporal orientation can be of utmost interest and importance to the business and administrative decision makers as it can provide an extra precious information for them to make informed decisions. In this paper, we propose a very first study to demonstrate the association between the sentiment view of the temporal orientation of the users and their different psycho-demographic attributes by analyzing their tweets. We first create a temporal orientation classifier in a minimally supervised way which classifies each tweet of the users in one of the three temporal categories, namely past, present, and future. A deep Bi-directional Long Short Term Memory (BLSTM) is used for the tweet classification task. Our tweet classifier achieves an accuracy of 78.27{\%} when tested on a manually created test set. We then determine the users{'} overall temporal orientation based on their tweets on the social media. The sentiment is added to the tweets at the fine-grained level where each temporal tweet is given a sentiment with either of the positive, negative or neutral. Our experiment reveals that depending upon the sentiment view of temporal orientation, a user{'}s attributes vary. We finally measure the correlation between the users{'} sentiment view of temporal orientation and their different psycho-demographic factors using regression.
Querying Word Embeddings for Similarity and Relatedness
Authors: Fatemeh Torabi Asr, Robert Zinkov, Michael Jones
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Word Embeddings, Word Similarity

Word embeddings obtained from neural network models such as Word2Vec Skipgram have become popular representations of word meaning and have been evaluated on a variety of word similarity and relatedness norming data. Skipgram generates a set of word and context embeddings, the latter typically discarded after training. We demonstrate the usefulness of context embeddings in predicting asymmetric association between words from a recently published dataset of production norms (Jouravlev {\&} McRae, 2016). Our findings suggest that humans respond with words closer to the cue within the context embedding space (rather than the word embedding space), when asked to generate thematically related words.
Accurate Text-Enhanced Knowledge Graph Representation Learning
Authors: Bo An, Bo Chen, Xianpei Han, Le Sun
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: General Classification, Graph Representation Learning, Knowledge Graphs, Link Prediction, Relation, Representation Learning, Triple Classification

Previous representation learning techniques for knowledge graph representation usually represent the same entity or relation in different triples with the same representation, without considering the ambiguity of relations and entities. To appropriately handle the semantic variety of entities/relations in distinct triples, we propose an accurate text-enhanced knowledge graph representation learning method, which can represent a relation/entity with different representations in different triples by exploiting additional textual information. Specifically, our method enhances representations by exploiting the entity descriptions and triple-specific relation mention. And a mutual attention mechanism between relation mention and entity description is proposed to learn more accurate textual representations for further improving knowledge graph representation. Experimental results show that our method achieves the state-of-the-art performance on both link prediction and triple classification tasks, and significantly outperforms previous text-enhanced knowledge representation models.
DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors
Authors: Prodromos Kolyvakis, Alex Kalousis, ros, Dimitris Kiritsis
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Entity Alignment, Feature Engineering, Ontology Matching, Representation Learning

Ontologies compartmentalize types and relations in a target domain and provide the semantic backbone needed for a plethora of practical applications. Very often different ontologies are developed independently for the same domain. Such {``}parallel{''} ontologies raise the need for a process that will establish alignments between their entities in order to unify and extend the existing knowledge. In this work, we present a novel entity alignment method which we dub DeepAlignment. DeepAlignment refines pre-trained word vectors aiming at deriving ontological entity descriptions which are tailored to the ontology matching task. The absence of explicit information relevant to the ontology matching task during the refinement process makes DeepAlignment completely unsupervised. We empirically evaluate our method using standard ontology matching benchmarks. We present significant performance improvements over the current state-of-the-art, demonstrating the advantages that representation learning techniques bring to ontology matching.
Efficient Sequence Learning with Group Recurrent Networks
Authors: Fei Gao, Lijun Wu, Li Zhao, Tao Qin, Xue-Qi Cheng, Tie-Yan Liu
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: GPU, Language Modeling, Language Modelling, Machine Translation, speech-recognition, Speech Recognition, Translation

Recurrent neural networks have achieved state-of-the-art results in many artificial intelligence tasks, such as language modeling, neural machine translation, speech recognition and so on. One of the key factors to these successes is big models. However, training such big models usually takes days or even weeks of time even if using tens of GPU cards. In this paper, we propose an efficient architecture to improve the efficiency of such RNN model training, which adopts the group strategy for recurrent layers, while exploiting the representation rearrangement strategy between layers as well as time steps. To demonstrate the advantages of our models, we conduct experiments on several datasets and tasks. The results show that our architecture achieves comparable or better accuracy comparing with baselines, with a much smaller number of parameters and at a much lower computational cost.
Nested Named Entity Recognition Revisited
Authors: Arzoo Katiyar, Claire Cardie
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Coreference Resolution, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Nested Named Entity Recognition, Opinion Mining, Relation Extraction

We propose a novel recurrent neural network-based approach to simultaneously handle nested named entity recognition and nested entity mention detection. The model learns a hypergraph representation for nested entities using features extracted from a recurrent neural network. In evaluations on three standard data sets, we show that our approach significantly outperforms existing state-of-the-art methods, which are feature-based. The approach is also efficient: it operates linearly in the number of tokens and the number of possible output labels at any token. Finally, we present an extension of our model that jointly learns the head of each entity mention.
Supervised Open Information Extraction
Authors: Gabriel Stanovsky, Julian Michael, Luke Zettlemoyer, Ido Dagan
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Knowledge Base Population, Natural Language Inference, Open Information Extraction, Question Answering, Semantic Role Labeling

We present data and methods that enable a supervised learning approach to Open Information Extraction (Open IE). Central to the approach is a novel formulation of Open IE as a sequence tagging problem, addressing challenges such as encoding multiple extractions for a predicate. We also develop a bi-LSTM transducer, extending recent deep Semantic Role Labeling models to extract Open IE tuples and provide confidence scores for tuning their precision-recall tradeoff. Furthermore, we show that the recently released Question-Answer Meaning Representation dataset can be automatically converted into an Open IE corpus which significantly increases the amount of available training data. Our supervised model outperforms the existing state-of-the-art Open IE systems on benchmark datasets.
Neural Syntactic Generative Models with Exact Marginalization
Authors: Jan Buys, Phil Blunsom
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Dependency Parsing, Language Modeling, Language Modelling, Transition-Based Dependency Parsing

We present neural syntactic generative models with exact marginalization that support both dependency parsing and language modeling. Exact marginalization is made tractable through dynamic programming over shift-reduce parsing and minimal RNN-based feature sets. Our algorithms complement previous approaches by supporting batched training and enabling online computation of next word probabilities. For supervised dependency parsing, our model achieves a state-of-the-art result among generative approaches. We also report empirical results on unsupervised syntactic models and their role in language modeling. We find that our model formulation of latent dependencies with exact marginalization do not lead to better intrinsic language modeling performance than vanilla RNNs, and that parsing accuracy is not correlated with language modeling perplexity in stack-based models.