pwc-archive/papers-with-abstracts huggingface dataset:
The Influence of Context on Sentence Acceptability Judgements
Authors: Jean-Philippe Bernardy, Shalom Lappin, Jey Han Lau
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Language Modeling, Language Modelling, Machine Translation, Sentence, Text Generation

We investigate the influence that document context exerts on human acceptability judgements for English sentences, via two sets of experiments. The first compares ratings for sentences presented on their own with ratings for the same set of sentences given in their document contexts. The second assesses the accuracy with which two types of neural models {---} one that incorporates context during training and one that does not {---} predict these judgements. Our results indicate that: (1) context improves acceptability ratings for ill-formed sentences, but also reduces them for well-formed sentences; and (2) context helps unsupervised systems to model acceptability.
Simplified Abugidas
Authors: Chenchen Ding, Masao Utiyama, Eiichiro Sumita
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Sentence

An abugida is a writing system where the consonant letters represent syllables with a default vowel and other vowels are denoted by diacritics. We investigate the feasibility of recovering the original text written in an abugida after omitting subordinate diacritics and merging consonant letters with similar phonetic values. This is crucial for developing more efficient input methods by reducing the complexity in abugidas. Four abugidas in the southern Brahmic family, i.e., Thai, Burmese, Khmer, and Lao, were studied using a newswire 20,000-sentence dataset. We compared the recovery performance of a support vector machine and an LSTM-based recurrent neural network, finding that the abugida graphemes could be recovered with 94{\%} - 97{\%} accuracy at the top-1 level and 98{\%} - 99{\%} at the top-4 level, even after omitting most diacritics (10 - 30 types) and merging the remaining 30 - 50 characters into 21 graphemes.
PhraseCTM: Correlated Topic Modeling on Phrases within Markov Random Fields
Authors: Weijing Huang
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Topic Models

Recent emerged phrase-level topic models are able to provide topics of phrases, which are easy to read for humans. But these models are lack of the ability to capture the correlation structure among the discovered numerous topics. We propose a novel topic model PhraseCTM and a two-stage method to find out the correlated topics at phrase level. In the first stage, we train PhraseCTM, which models the generation of words and phrases simultaneously by linking the phrases and component words within Markov Random Fields when they are semantically coherent. In the second stage, we generate the correlation of topics from PhraseCTM. We evaluate our method by a quantitative experiment and a human study, showing the correlated topic modeling on phrases is a good and practical way to interpret the underlying themes of a corpus.
A Document Descriptor using Covariance of Word Vectors
Authors: Marwan Torki
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7

In this paper, we address the problem of finding a novel document descriptor based on the covariance matrix of the word vectors of a document. Our descriptor has a fixed length, which makes it easy to use in many supervised and unsupervised applications. We tested our novel descriptor in different tasks including supervised and unsupervised settings. Our evaluation shows that our document covariance descriptor fits different tasks with competitive performance against state-of-the-art methods.
Learning with Structured Representations for Negation Scope Extraction
Authors: Hao Li, Wei Lu
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Negation, Sentiment Analysis

We report an empirical study on the task of negation scope extraction given the negation cue. Our key observation is that certain useful information such as features related to negation cue, long-distance dependencies as well as some latent structural information can be exploited for such a task. We design approaches based on conditional random fields (CRF), semi-Markov CRF, as well as latent-variable CRF models to capture such information. Extensive experiments on several standard datasets demonstrate that our approaches are able to achieve better results than existing approaches reported in the literature.
Sense-Aware Neural Models for Pun Location in Texts
Authors: Yitao Cai, Yin Li, Xiaojun Wan
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Word Sense Disambiguation

A homographic pun is a form of wordplay in which one signifier (usually a word) suggests two or more meanings by exploiting polysemy for an intended humorous or rhetorical effect. In this paper, we focus on the task of pun location, which aims to identify the pun word in a given short text. We propose a sense-aware neural model to address this challenging task. Our model first obtains several WSD results for the text, and then leverages a bidirectional LSTM network to model each sequence of word senses. The outputs at each time step for different LSTM networks are then concatenated for prediction. Evaluation results on the benchmark SemEval 2017 dataset demonstrate the efficacy of our proposed model.
Addressing Noise in Multidialectal Word Embeddings
Authors: Alex Erdmann, er, Nasser Zalmout, Nizar Habash
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Sentence, Transliteration, Word Embeddings

Word embeddings are crucial to many natural language processing tasks. The quality of embeddings relies on large non-noisy corpora. Arabic dialects lack large corpora and are noisy, being linguistically disparate with no standardized spelling. We make three contributions to address this noise. First, we describe simple but effective adaptations to word embedding tools to maximize the informative content leveraged in each training sentence. Second, we analyze methods for representing disparate dialects in one embedding space, either by mapping individual dialects into a shared space or learning a joint model of all dialects. Finally, we evaluate via dictionary induction, showing that two metrics not typically reported in the task enable us to analyze our contributions{'} effects on low and high frequency words. In addition to boosting performance between 2-53{\%}, we specifically improve on noisy, low frequency forms without compromising accuracy on high frequency forms.
GNEG: Graph-Based Negative Sampling for word2vec
Authors: Zheng Zhang, Pierre Zweigenbaum
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Language Modeling, Language Modelling, Representation Learning, Word Similarity

Negative sampling is an important component in word2vec for distributed word representation learning. We hypothesize that taking into account global, corpus-level information and generating a different noise distribution for each target word better satisfies the requirements of negative examples for each training word than the original frequency-based distribution. In this purpose we pre-compute word co-occurrence statistics from the corpus and apply to it network algorithms such as random walk. We test this hypothesis through a set of experiments whose results show that our approach boosts the word analogy task by about 5{\%} and improves the performance on word similarity tasks by about 1{\%} compared to the skip-gram negative sampling baseline.
Modeling Sentiment Association in Discourse for Humor Recognition
Authors: Lizhen Liu, Donghai Zhang, Wei Song
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Common Sense Reasoning, Sentiment Analysis

Humor is one of the most attractive parts in human communication. However, automatically recognizing humor in text is challenging due to the complex characteristics of humor. This paper proposes to model sentiment association between discourse units to indicate how the punchline breaks the expectation of the setup. We found that discourse relation, sentiment conflict and sentiment transition are effective indicators for humor recognition. On the perspective of using sentiment related features, sentiment association in discourse is more useful than counting the number of emotional words.
Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining
Authors: Eyal Shnarch, Carlos Alzate, Lena Dankin, Martin Gleize, Yufang Hou, Leshem Choshen, Ranit Aharonov, Noam Slonim
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Information Retrieval, Natural Language Understanding, Relation Extraction, Sarcasm Detection, Sentiment Analysis

The process of obtaining high quality labeled data for natural language understanding tasks is often slow, error-prone, complicated and expensive. With the vast usage of neural networks, this issue becomes more notorious since these networks require a large amount of labeled data to produce satisfactory results. We propose a methodology to blend high quality but scarce strong labeled data with noisy but abundant weak labeled data during the training of neural networks. Experiments in the context of topic-dependent evidence detection with two forms of weak labeled data show the advantages of the blending scheme. In addition, we provide a manually annotated data set for the task of topic-dependent evidence detection. We believe that blending weak and strong labeled data is a general notion that may be applicable to many language understanding tasks, and can especially assist researchers who wish to train a network but have a small amount of high quality labeled data for their task of interest.
Investigating Audio, Video, and Text Fusion Methods for End-to-End Automatic Personality Prediction
Authors: Onno Kampman, Elham J. Barezi, Dario Bertero, Pascale Fung
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Emotional Intelligence

We propose a tri-modal architecture to predict Big Five personality trait scores from video clips with different channels for audio, text, and video data. For each channel, stacked Convolutional Neural Networks are employed. The channels are fused both on decision-level and by concatenating their respective fully connected layers. It is shown that a multimodal fusion approach outperforms each single modality channel, with an improvement of 9.4{\%} over the best individual modality (video). Full backpropagation is also shown to be better than a linear combination of modalities, meaning complex interactions between modalities can be leveraged to build better models. Furthermore, we can see the prediction relevance of each modality for each trait. The described model can be used to increase the emotional intelligence of virtual agents.
An Empirical Study of Building a Strong Baseline for Constituency Parsing
Authors: Jun Suzuki, Sho Takase, Hidetaka Kamigaito, Makoto Morishita, Masaaki Nagata
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Abstractive Text Summarization, Constituency Parsing, Machine Translation, Text Generation, Translation

This paper investigates the construction of a strong baseline based on general purpose sequence-to-sequence models for constituency parsing. We incorporate several techniques that were mainly developed in natural language generation tasks, e.g., machine translation and summarization, and demonstrate that the sequence-to-sequence model achieves the current top-notch parsers{'} performance (almost) without requiring any explicit task-specific knowledge or architecture of constituent parsing.
Exploring Semantic Properties of Sentence Embeddings
Authors: Xunjie Zhu, Tingfeng Li, Gerard de Melo
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Translation, Reading Comprehension, Semantic Textual Similarity, Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings

Neural vector representations are ubiquitous throughout all subfields of NLP. While word vectors have been studied in much detail, thus far only little light has been shed on the properties of sentence embeddings. In this paper, we assess to what extent prominent sentence embedding methods exhibit select semantic properties. We propose a framework that generate triplets of sentences to explore how changes in the syntactic structure or semantics of a given sentence affect the similarities obtained between their sentence embeddings.
Extracting Commonsense Properties from Embeddings with Limited Human Guidance
Authors: Yiben Yang, Larry Birnbaum, Ji-Ping Wang, Doug Downey
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Active Learning, Common Sense Reasoning, Zero-Shot Learning

Intelligent systems require common sense, but automatically extracting this knowledge from text can be difficult. We propose and assess methods for extracting one type of commonsense knowledge, object-property comparisons, from pre-trained embeddings. In experiments, we show that our approach exceeds the accuracy of previous work but requires substantially less hand-annotated knowledge. Further, we show that an active learning approach that synthesizes common-sense queries can boost accuracy.
Adaptive Knowledge Sharing in Multi-Task Learning: Improving Low-Resource Neural Machine Translation
Authors: Poorya Zaremoodi, Wray Buntine, Gholamreza Haffari
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Inductive Bias, Low Resource Neural Machine Translation, Low-Resource Neural Machine Translation, Machine Translation, Multi-Task Learning, NMT, Translation

Neural Machine Translation (NMT) is notorious for its need for large amounts of bilingual data. An effective approach to compensate for this requirement is Multi-Task Learning (MTL) to leverage different linguistic resources as a source of inductive bias. Current MTL architectures are based on the Seq2Seq transduction, and (partially) share different components of the models among the tasks. However, this MTL approach often suffers from task interference and is not able to fully capture commonalities among subsets of tasks. We address this issue by extending the recurrent units with multiple {``}blocks{''} along with a trainable {``}routing network{''}. The routing network enables adaptive collaboration by dynamic sharing of blocks conditioned on the task at hand, input, and model state. Empirical evaluation of two low-resource translation tasks, English to Vietnamese and Farsi, show +1 BLEU score improvements compared to strong baselines.
diaNED: Time-Aware Named Entity Disambiguation for Diachronic Corpora
Authors: Prabal Agarwal, Jannik Str{\"o}tgen, Luciano del Corro, Johannes Hoffart, Gerhard Weikum
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Articles, Entity Disambiguation

Named Entity Disambiguation (NED) systems perform well on news articles and other texts covering a specific time interval. However, NED quality drops when inputs span long time periods like in archives or historic corpora. This paper presents the first time-aware method for NED that resolves ambiguities even when mention contexts give only few cues. The method is based on computing temporal signatures for entities and comparing these to the temporal contexts of input mentions. Our experiments show superior quality on a newly created diachronic corpus.
Examining Temporality in Document Classification
Authors: Xiaolei Huang, Michael J. Paul
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Classification, Document Classification, Domain Adaptation, General Classification

Many corpora span broad periods of time. Language processing models trained during one time period may not work well in future time periods, and the best model may depend on specific times of year (e.g., people might describe hotels differently in reviews during the winter versus the summer). This study investigates how document classifiers trained on documents from certain time intervals perform on documents from other time intervals, considering both seasonal intervals (intervals that repeat across years, e.g., winter) and non-seasonal intervals (e.g., specific years). We show experimentally that classification performance varies over time, and that performance can be improved by using a standard domain adaptation approach to adjust for changes in time.
Personalized Review Generation By Expanding Phrases and Attending on Aspect-Aware Representations
Authors: Jianmo Ni, Julian McAuley
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Decoder, Recommendation Systems, Review Generation, Sentiment Analysis, Text Generation

In this paper, we focus on the problem of building assistive systems that can help users to write reviews. We cast this problem using an encoder-decoder framework that generates personalized reviews by expanding short phrases (e.g. review summaries, product titles) provided as input to the system. We incorporate aspect-level information via an aspect encoder that learns aspect-aware user and item representations. An attention fusion layer is applied to control generation by attending on the outputs of multiple encoders. Experimental results show that our model successfully learns representations capable of generating coherent and diverse reviews. In addition, the learned aspect-aware representations discover those aspects that users are more inclined to discuss and bias the generated text toward their personalized aspect preferences.
Learning Simplifications for Specific Target Audiences
Authors: Carolina Scarton, Lucia Specia
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Lexical Simplification, Machine Translation, Sentence, Text Simplification, Translation, Zero-Shot Learning

Text simplification (TS) is a monolingual text-to-text transformation task where an original (complex) text is transformed into a target (simpler) text. Most recent work is based on sequence-to-sequence neural models similar to those used for machine translation (MT). Different from MT, TS data comprises more elaborate transformations, such as sentence splitting. It can also contain multiple simplifications of the same original text targeting different audiences, such as school grade levels. We explore these two features of TS to build models tailored for specific grade levels. Our approach uses a standard sequence-to-sequence architecture where the original sequence is annotated with information about the target audience and/or the (predicted) type of simplification operation. We show that it outperforms state-of-the-art TS approaches (up to 3 and 12 BLEU and SARI points, respectively), including when training data for the specific complex-simple combination of grade levels is not available, i.e. zero-shot learning.
Split and Rephrase: Better Evaluation and Stronger Baselines
Authors: Roee Aharoni, Yoav Goldberg
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Translation, Memorization, Sentence, Split and Rephrase

Splitting and rephrasing a complex sentence into several shorter sentences that convey the same meaning is a challenging problem in NLP. We show that while vanilla seq2seq models can reach high scores on the proposed benchmark (Narayan et al., 2017), they suffer from memorization of the training set which contains more than 89{\%} of the unique simple sentences from the validation and test sets. To aid this, we present a new train-development-test data split and neural models augmented with a copy-mechanism, outperforming the best reported baseline by 8.68 BLEU and fostering further progress on the task.
Tackling the Story Ending Biases in The Story Cloze Test
Authors: Rishi Sharma, James Allen, Bakhsh, Omid eh, Nasrin Mostafazadeh
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Benchmarking, Cloze Test

The Story Cloze Test (SCT) is a recent framework for evaluating story comprehension and script learning. There have been a variety of models tackling the SCT so far. Although the original goal behind the SCT was to require systems to perform deep language understanding and commonsense reasoning for successful narrative understanding, some recent models could perform significantly better than the initial baselines by leveraging human-authorship biases discovered in the SCT dataset. In order to shed some light on this issue, we have performed various data analysis and analyzed a variety of top performing models presented for this task. Given the statistics we have aggregated, we have designed a new crowdsourcing scheme that creates a new SCT dataset, which overcomes some of the biases. We benchmark a few models on the new dataset and show that the top-performing model on the original SCT dataset fails to keep up its performance. Our findings further signify the importance of benchmarking NLP systems on various evolving test sets.
Pretraining Sentiment Classifiers with Unlabeled Dialog Data
Authors: Toru Shimizu, Nobuyuki Shimizu, Hayato Kobayashi
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Classification, Decoder, General Classification, Language Modeling, Language Modelling, Sentiment Analysis, Sentiment Classification

The huge cost of creating labeled training data is a common problem for supervised learning tasks such as sentiment classification. Recent studies showed that pretraining with unlabeled data via a language model can improve the performance of classification models. In this paper, we take the concept a step further by using a conditional language model, instead of a language model. Specifically, we address a sentiment classification task for a tweet analysis service as a case study and propose a pretraining strategy with unlabeled dialog data (tweet-reply pairs) via an encoder-decoder model. Experimental results show that our strategy can improve the performance of sentiment classifiers and outperform several state-of-the-art strategies including language model pretraining.
Disambiguating False-Alarm Hashtag Usages in Tweets for Irony Detection
Authors: Hen-Hsen Huang, Chiao-Chen Chen, Hsin-Hsi Chen
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Opinion Mining, Sentiment Analysis, Stance Detection

The reliability of self-labeled data is an important issue when the data are regarded as ground-truth for training and testing learning-based models. This paper addresses the issue of false-alarm hashtags in the self-labeled data for irony detection. We analyze the ambiguity of hashtag usages and propose a novel neural network-based model, which incorporates linguistic information from different aspects, to disambiguate the usage of three hashtags that are widely used to collect the training data for irony detection. Furthermore, we apply our model to prune the self-labeled training data. Experimental results show that the irony detection model trained on the less but cleaner training instances outperforms the models trained on all data.
`Lighter' Can Still Be Dark: Modeling Comparative Color Descriptions
Authors: Olivia Winn, Smar Muresan, a
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Object Recognition

We propose a novel paradigm of grounding comparative adjectives within the realm of color descriptions. Given a reference RGB color and a comparative term (e.g., lighter, darker), our model learns to ground the comparative as a direction in the RGB space such that the colors along the vector, rooted at the reference color, satisfy the comparison. Our model generates grounded representations of comparative adjectives with an average accuracy of 0.65 cosine similarity to the desired direction of change. These vectors approach colors with Delta-E scores of under 7 compared to the target colors, indicating the differences are very small with respect to human perception. Our approach makes use of a newly created dataset for this task derived from existing labeled color data.