pwc-archive/papers-with-abstracts huggingface dataset:
Methodical Evaluation of Arabic Word Embeddings
Authors: Mohammed Elrazzaz, Shady Elbassuoni, Khaled Shaban, Chadi Helwe
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Document Classification, Learning Word Embeddings, Named Entity Recognition (NER), Word Embeddings

Many unsupervised learning techniques have been proposed to obtain meaningful representations of words from text. In this study, we evaluate these various techniques when used to generate Arabic word embeddings. We first build a benchmark for the Arabic language that can be utilized to perform intrinsic evaluation of different word embeddings. We then perform additional extrinsic evaluations of the embeddings based on two NLP tasks.
Multilingual Connotation Frames: A Case Study on Social Media for Targeted Sentiment Analysis and Forecast
Authors: Hannah Rashkin, Eric Bell, Yejin Choi, Svitlana Volkova
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Sentiment Analysis

People around the globe respond to major real world events through social media. To study targeted public sentiments across many languages and geographic locations, we introduce multilingual connotation frames: an extension from English connotation frames of Rashkin et al. (2016) with 10 additional European languages, focusing on the implied sentiments among event participants engaged in a frame. As a case study, we present large scale analysis on targeted public sentiments toward salient events and entities using 1.2 million multilingual connotation frames extracted from Twitter.
Demographic Inference on Twitter using Recursive Neural Networks
Authors: Sunghwan Mac Kim, Qiongkai Xu, Lizhen Qu, Stephen Wan, C{\'e}cile Paris
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Network Embedding

In social media, demographic inference is a critical task in order to gain a better understanding of a cohort and to facilitate interacting with one{'}s audience. Most previous work has made independence assumptions over topological, textual and label information on social networks. In this work, we employ recursive neural networks to break down these independence assumptions to obtain inference about demographic characteristics on Twitter. We show that our model performs better than existing models including the state-of-the-art.
Twitter Demographic Classification Using Deep Multi-modal Multi-task Learning
Authors: Prashanth Vijayaraghavan, Soroush Vosoughi, Deb Roy
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Classification, General Classification, Multi-Task Learning

Twitter should be an ideal place to get a fresh read on how different issues are playing with the public, one that{'}s potentially more reflective of democracy in this new media age than traditional polls. Pollsters typically ask people a fixed set of questions, while in social media people use their own voices to speak about whatever is on their minds. However, the demographic distribution of users on Twitter is not representative of the general population. In this paper, we present a demographic classifier for gender, age, political orientation and location on Twitter. We collected and curated a robust Twitter demographic dataset for this task. Our classifier uses a deep multi-modal multi-task learning architecture to reach a state-of-the-art performance, achieving an F1-score of 0.89, 0.82, 0.86, and 0.68 for gender, age, political orientation, and location respectively.
A Network Framework for Noisy Label Aggregation in Social Media
Authors: Xueying Zhan, Yao-Wei Wang, Yanghui Rao, Haoran Xie, Qing Li, Fu Lee Wang, Tak-Lam Wong
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Cultural Vocal Bursts Intensity Prediction, Image Classification, Learning-To-Rank, Topic Models

This paper focuses on the task of noisy label aggregation in social media, where users with different social or culture backgrounds may annotate invalid or malicious tags for documents. To aggregate noisy labels at a small cost, a network framework is proposed by calculating the matching degree of a document{'}s topics and the annotators{'} meta-data. Unlike using the back-propagation algorithm, a probabilistic inference approach is adopted to estimate network parameters. Finally, a new simulation method is designed for validating the effectiveness of the proposed framework in aggregating noisy labels.
Parser Adaptation for Social Media by Integrating Normalization
Authors: Rob van der Goot, Gertjan van Noord
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Domain Adaptation, Named Entity Recognition (NER), Sentiment Analysis

This work explores different approaches of using normalization for parser adaptation. Traditionally, normalization is used as separate pre-processing step. We show that integrating the normalization model into the parsing algorithm is more beneficial. This way, multiple normalization candidates can be leveraged, which improves parsing performance on social media. We test this hypothesis by modifying the Berkeley parser; out-of-the-box it achieves an F1 score of 66.52. Our integrated approach reaches a significant improvement with an F1 score of 67.36, while using the best normalization sequence results in an F1 score of only 66.94.
AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine
Authors: Minghui Qiu, Feng-Lin Li, Siyu Wang, Xing Gao, Yan Chen, Weipeng Zhao, Haiqing Chen, Jun Huang, Wei Chu
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Chatbot, Information Retrieval, Retrieval

We propose AliMe Chat, an open-domain chatbot engine that integrates the joint results of Information Retrieval (IR) and Sequence to Sequence (Seq2Seq) based generation models. AliMe Chat uses an attentive Seq2Seq based rerank model to optimize the joint results. Extensive experiments show our engine outperforms both IR and generation based models. We launch AliMe Chat for a real-world industrial application and observe better results than another public chatbot.
Self-Crowdsourcing Training for Relation Extraction
Authors: Azad Abad, Moin Nabi, Aless Moschitti, ro
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Question Answering, Relation, Relation Extraction, Text Summarization

In this paper we introduce a self-training strategy for crowdsourcing. The training examples are automatically selected to train the crowd workers. Our experimental results show an impact of 5{\%} Improvement in terms of F1 for relation extraction task, compared to the method based on distant supervision.
A Generative Attentional Neural Network Model for Dialogue Act Classification
Authors: Quan Hung Tran, Gholamreza Haffari, Ingrid Zukerman
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Classification, Dialogue Act Classification, General Classification, Machine Translation

We propose a novel generative neural network architecture for Dialogue Act classification. Building upon the Recurrent Neural Network framework, our model incorporates a novel attentional technique and a label to label connection for sequence learning, akin to Hidden Markov Models. The experiments show that both of these innovations lead our model to outperform strong baselines for dialogue act classification on MapTask and Switchboard corpora. We further empirically analyse the effectiveness of each of the new innovations.
Salience Rank: Efficient Keyphrase Extraction with Topic Modeling
Authors: Nedelina Teneva, Weiwei Cheng
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Keyphrase Extraction, Part-Of-Speech Tagging, Specificity

Topical PageRank (TPR) uses latent topic distribution inferred by Latent Dirichlet Allocation (LDA) to perform ranking of noun phrases extracted from documents. The ranking procedure consists of running PageRank K times, where K is the number of topics used in the LDA model. In this paper, we propose a modification of TPR, called Salience Rank. Salience Rank only needs to run PageRank once and extracts comparable or better keyphrases on benchmark datasets. In addition to quality and efficiency benefit, our method has the flexibility to extract keyphrases with varying tradeoffs between topic specificity and corpus specificity.
List-only Entity Linking
Authors: Ying Lin, Chin-Yew Lin, Heng Ji
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Entity Linking

Traditional Entity Linking (EL) technologies rely on rich structures and properties in the target knowledge base (KB). However, in many applications, the KB may be as simple and sparse as lists of names of the same type (e.g., lists of products). We call it as List-only Entity Linking problem. Fortunately, some mentions may have more cues for linking, which can be used as seed mentions to bridge other mentions and the uninformative entities. In this work, we select most linkable mentions as seed mentions and disambiguate other mentions by comparing them with the seed mentions rather than directly with the entities. Our experiments on linking mentions to seven automatically mined lists show promising results and demonstrate the effectiveness of our approach.
Improving Native Language Identification by Using Spelling Errors
Authors: Lingzhen Chen, Carlo Strapparava, Vivi Nastase
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Language Identification, Native Language Identification

In this paper, we explore spelling errors as a source of information for detecting the native language of a writer, a previously under-explored area. We note that character n-grams from misspelled words are very indicative of the native language of the author. In combination with other lexical features, spelling error features lead to 1.2{\%} improvement in accuracy on classifying texts in the TOEFL11 corpus by the author{'}s native language, compared to systems participating in the NLI shared task.
Sentence Embedding for Neural Machine Translation Domain Adaptation
Authors: Rui Wang, Andrew Finch, Masao Utiyama, Eiichiro Sumita
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Domain Adaptation, Language Modeling, Language Modelling, Machine Translation, NMT, Sentence, Sentence Embedding, Sentence-Embedding, Translation

Although new corpora are becoming increasingly available for machine translation, only those that belong to the same or similar domains are typically able to improve translation performance. Recently Neural Machine Translation (NMT) has become prominent in the field. However, most of the existing domain adaptation methods only focus on phrase-based machine translation. In this paper, we exploit the NMT{'}s internal embedding of the source sentence and use the sentence embedding similarity to select the sentences which are close to in-domain data. The empirical adaptation results on the IWSLT English-French and NIST Chinese-English tasks show that the proposed methods can substantially improve NMT performance by 2.4-9.0 BLEU points, outperforming the existing state-of-the-art baseline by 2.3-4.5 BLEU points.
Speeding Up Neural Machine Translation Decoding by Shrinking Run-time Vocabulary
Authors: Xing Shi, Kevin Knight
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: GPU, Machine Translation, NMT, Translation

We speed up Neural Machine Translation (NMT) decoding by shrinking run-time target vocabulary. We experiment with two shrinking approaches: Locality Sensitive Hashing (LSH) and word alignments. Using the latter method, we get a 2x overall speed-up over a highly-optimized GPU implementation, without hurting BLEU. On certain low-resource language pairs, the same methods improve BLEU by 0.5 points. We also report a negative result for LSH on GPUs, due to relatively large overhead, though it was successful on CPUs. Compared with Locality Sensitive Hashing (LSH), decoding with word alignments is GPU-friendly, orthogonal to existing speedup methods and more robust across language pairs.
EuroSense: Automatic Harvesting of Multilingual Sense Annotations from Parallel Text
Authors: Claudio Delli Bovi, Jose Camacho-Collados, Aless Raganato, ro, Roberto Navigli
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Entity Linking, Machine Translation, Translation, Word Sense Disambiguation

Parallel corpora are widely used in a variety of Natural Language Processing tasks, from Machine Translation to cross-lingual Word Sense Disambiguation, where parallel sentences can be exploited to automatically generate high-quality sense annotations on a large scale. In this paper we present EuroSense, a multilingual sense-annotated resource based on the joint disambiguation of the Europarl parallel corpus, with almost 123 million sense annotations for over 155 thousand distinct concepts and entities from a language-independent unified sense inventory. We evaluate the quality of our sense annotations intrinsically and extrinsically, showing their effectiveness as training data for Word Sense Disambiguation.
Pay Attention to the Ending:Strong Neural Baselines for the ROC Story Cloze Task
Authors: Zheng Cai, Lifu Tu, Kevin Gimpel
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Negation, Outlier Detection, Sentiment Analysis

We consider the ROC story cloze task (Mostafazadeh et al., 2016) and present several findings. We develop a model that uses hierarchical recurrent networks with attention to encode the sentences in the story and score candidate endings. By discarding the large training set and only training on the validation set, we achieve an accuracy of 74.7{\%}. Even when we discard the story plots (sentences before the ending) and only train to choose the better of two endings, we can still reach 72.5{\%}. We then analyze this {``}ending-only{''} task setting. We estimate human accuracy to be 78{\%} and find several types of clues that lead to this high accuracy, including those related to sentiment, negation, and general ending likelihood regardless of the story context.
Determining Whether and When People Participate in the Events They Tweet About
Authors: Krishna Chaitanya Sanagavarapu, Alakan Vempala, a, Eduardo Blanco
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7

This paper describes an approach to determine whether people participate in the events they tweet about. Specifically, we determine whether people are participants in events with respect to the tweet timestamp. We target all events expressed by verbs in tweets, including past, present and events that may occur in the future. We present new annotations using 1,096 event mentions, and experimental results showing that the task is challenging.
Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter
Authors: Svitlana Volkova, Kyle Shaffer, Jin Yea Jang, Nathan Hodas
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Deception Detection

Pew research polls report 62 percent of U.S. adults get news on social media (Gottfried and Shearer, 2016). In a December poll, 64 percent of U.S. adults said that {``}made-up news{''} has caused a {``}great deal of confusion{''} about the facts of current events (Barthel et al., 2016). Fabricated stories in social media, ranging from deliberate propaganda to hoaxes and satire, contributes to this confusion in addition to having serious effects on global stability. In this work we build predictive models to classify 130 thousand news posts as suspicious or verified, and predict four sub-types of suspicious news {--} satire, hoaxes, clickbait and propaganda. We show that neural network models trained on tweet content and social network interactions outperform lexical models. Unlike previous work on deception detection, we find that adding syntax and grammar features to our models does not improve performance. Incorporating linguistic features improves classification results, however, social interaction features are most informative for finer-grained separation between four types of suspicious news posts.
Temporal Orientation of Tweets for Predicting Income of Users
Authors: Mohammed Hasanuzzaman, Sabyasachi Kamila, M Kaur, eep, Sriparna Saha, Asif Ekbal
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: regression, TAG, Weakly-supervised Learning

Automatically estimating a user{'}s socio-economic profile from their language use in social media can significantly help social science research and various downstream applications ranging from business to politics. The current paper presents the first study where user cognitive structure is used to build a predictive model of income. In particular, we first develop a classifier using a weakly supervised learning framework to automatically time-tag tweets as past, present, or future. We quantify a user{'}s overall temporal orientation based on their distribution of tweets, and use it to build a predictive model of income. Our analysis uncovers a correlation between future temporal orientation and income. Finally, we measure the predictive power of future temporal orientation on income by performing regression.
Character-Aware Neural Morphological Disambiguation
Authors: Alymzhan Toleu, Gulmira Tolegen, Aibek Makazhanov
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Morphological Disambiguation, Word Embeddings

We develop a language-independent, deep learning-based approach to the task of morphological disambiguation. Guided by the intuition that the correct analysis should be {``}most similar{''} to the context, we propose dense representations for morphological analyses and surface context and a simple yet effective way of combining the two to perform disambiguation. Our approach improves on the language-dependent state of the art for two agglutinative languages (Turkish and Kazakh) and can be potentially applied to other morphologically complex languages.
How (not) to train a dependency parser: The curious case of jackknifing part-of-speech taggers
Authors: {\v{Z}}eljko Agi{\'c}, Natalie Schluter
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Dependency Parsing

In dependency parsing, jackknifing taggers is indiscriminately used as a simple adaptation strategy. Here, we empirically evaluate when and how (not) to use jackknifing in parsing. On 26 languages, we reveal a preference that conflicts with, and surpasses the ubiquitous ten-folding. We show no clear benefits of tagging the training data in cross-lingual parsing.
NLP for Precision Medicine
Authors: Hoifung Poon, Chris Quirk, Kristina Toutanova, Wen-tau Yih
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Decision Making, Entity Linking, Relation Extraction, Semantic Parsing

We will introduce precision medicine and showcase the vast opportunities for NLP in this burgeoning field with great societal impact. We will review pressing NLP problems, state-of-the art methods, and important applications, as well as datasets, medical resources, and practical issues. The tutorial will provide an accessible overview of biomedicine, and does not presume knowledge in biology or healthcare. The ultimate goal is to reduce the entry barrier for NLP researchers to contribute to this exciting domain.
Multimodal Machine Learning: Integrating Language, Vision and Speech
Authors: Louis-Philippe Morency, Tadas Baltru{\v{s}}aitis
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Audio-Visual Speech Recognition, BIG-bench Machine Learning, Image Captioning, Question Answering, Representation Learning, speech-recognition, Speech Recognition, Translation, Video Captioning, Visual Question Answering (VQA), Visual Speech Recognition, Zero-Shot Learning

Multimodal machine learning is a vibrant multi-disciplinary research field which addresses some of the original goals of artificial intelligence by integrating and modeling multiple communicative modalities, including linguistic, acoustic and visual messages. With the initial research on audio-visual speech recognition and more recently with image and video captioning projects, this research field brings some unique challenges for multimodal researchers given the heterogeneity of the data and the contingency often found between modalities.This tutorial builds upon a recent course taught at Carnegie Mellon University during the Spring 2016 semester (CMU course 11-777) and two tutorials presented at CVPR 2016 and ICMI 2016. The present tutorial will review fundamental concepts of machine learning and deep neural networks before describing the five main challenges in multimodal machine learning: (1) multimodal representation learning, (2) translation {\&} mapping, (3) modality alignment, (4) multimodal fusion and (5) co-learning. The tutorial will also present state-of-the-art algorithms that were recently proposed to solve multimodal applications such as image captioning, video descriptions and visual question-answer. We will also discuss the current and upcoming challenges.
Deep Learning for Semantic Composition
Authors: Xiaodan Zhu, Edward Grefenstette
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Deep Learning, Semantic Composition

Learning representation to model the meaning of text has been a core problem in NLP. The last several years have seen extensive interests on distributional approaches, in which text spans of different granularities are encoded as vectors of numerical values. If properly learned, such representation has showed to achieve the state-of-the-art performance on a wide range of NLP problems.In this tutorial, we will cover the fundamentals and the state-of-the-art research on neural network-based modeling for semantic composition, which aims to learn distributed representation for different granularities of text, e.g., phrases, sentences, or even documents, from their sub-component meaning representation, e.g., word embedding.
Beyond Words: Deep Learning for Multiword Expressions and Collocations
Authors: Valia Kordoni
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Deep Learning, Information Retrieval, Machine Translation

Deep learning has recently shown much promise for NLP applications. Traditionally, in most NLP approaches, documents or sentences are represented by a sparse bag-of-words representation. There is now a lot of work which goes beyond this by adopting a distributed representation of words, by constructing a so-called ``neural embedding'' or vector space representation of each word or document. The aim of this tutorial is to go beyond the learning of word vectors and present methods for learning vector representations for Multiword Expressions and bilingual phrase pairs, all of which are useful for various NLP applications.This tutorial aims to provide attendees with a clear notion of the linguistic and distributional characteristics of Multiword Expressions (MWEs), their relevance for the intersection of deep learning and natural language processing, what methods and resources are available to support their use, and what more could be done in the future. Our target audience are researchers and practitioners in machine learning, parsing (syntactic and semantic) and language technology, not necessarily experts in MWEs, who are interested in tasks that involve or could benefit from considering MWEs as a pervasive phenomenon in human language and communication.