pwc-archive/papers-with-abstracts huggingface dataset:
Deep Neural Networks at the Service of Multilingual Parallel Sentence Extraction
Authors: Ahmad Aghaebrahimian
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Information Retrieval, Machine Translation, Sentence

Wikipedia provides an invaluable source of parallel multilingual data, which are in high demand for various sorts of linguistic inquiry, including both theoretical and practical studies. We introduce a novel end-to-end neural model for large-scale parallel data harvesting from Wikipedia. Our model is language-independent, robust, and highly scalable. We use our system for collecting parallel German-English, French-English and Persian-English sentences. Human evaluations at the end show the strong performance of this model in collecting high-quality parallel data. We also propose a statistical framework which extends the results of our human evaluation to other language pairs. Our model also obtained a state-of-the-art result on the German-English dataset of BUCC 2017 shared task on parallel sentence extraction from comparable corpora.
Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention
Authors: Ruifang He, Xuefei Zhang, Di Jin, Longbiao Wang, Jianwu Dang, Xiangang Li
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Network Embedding, Representation Learning, Topic Models, Variational Inference

Traditional topic models are insufficient for topic extraction in social media. The existing methods only consider text information or simultaneously model the posts and the static characteristics of social media. They ignore that one discusses diverse topics when dynamically interacting with different people. Moreover, people who talk about the same topic have different effects on the topic. In this paper, we propose an Interaction-Aware Topic Model (IATM) for microblog conversations by integrating network embedding and user attention. A conversation network linking users based on reposting and replying relationship is constructed to mine the dynamic user behaviours. We model dynamic interactions and user attention so as to learn interaction-aware edge embeddings with social context. Then they are incorporated into neural variational inference for generating the more consistent topics. The experiments on three real-world datasets show that our proposed model is effective.
Cross-media User Profiling with Joint Textual and Social User Embedding
Authors: Jingjing Wang, Shoushan Li, Mingqi Jiang, Hanqian Wu, Guodong Zhou
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Classification, Gender Classification, General Classification, regression

In realistic scenarios, a user profiling model (e.g., gender classification or age regression) learned from one social media might perform rather poorly when tested on another social media due to the different data distributions in the two media. In this paper, we address cross-media user profiling by bridging the knowledge between the source and target media with a uniform user embedding learning approach. In our approach, we first construct a cross-media user-word network to capture the relationship among users through the textual information and a modified cross-media user-user network to capture the relationship among users through the social information. Then, we learn user embedding by jointly learning the heterogeneous network composed of above two networks. Finally, we train a classification (or regression) model with the obtained user embeddings as input to perform user profiling. Empirical studies demonstrate the effectiveness of the proposed approach to two cross-media user profiling tasks, i.e., cross-media gender classification and cross-media age regression.
Incorporating Syntactic Uncertainty in Neural Machine Translation with a Forest-to-Sequence Model
Authors: Poorya Zaremoodi, Gholamreza Haffari
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, NMT, Sentence, Translation

Incorporating syntactic information in Neural Machine Translation (NMT) can lead to better reorderings, particularly useful when the language pairs are syntactically highly divergent or when the training bitext is not large. Previous work on using syntactic information, provided by top-1 parse trees generated by (inevitably error-prone) parsers, has been promising. In this paper, we propose a forest-to-sequence NMT model to make use of exponentially many parse trees of the source sentence to compensate for the parser errors. Our method represents the collection of parse trees as a packed forest, and learns a neural transducer to translate from the input forest to the target sentence. Experiments on English to German, Chinese and Farsi translation tasks show the superiority of our approach over the sequence-to-sequence and tree-to-sequence neural translation models.
Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization
Authors: Haoran Li, Junnan Zhu, Jiajun Zhang, Cheng-qing Zong
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Abstractive Text Summarization, Decoder, Informativeness, Sentence, Sentence Summarization, Summarization, Text Summarization

In this paper, we investigate the sentence summarization task that produces a summary from a source sentence. Neural sequence-to-sequence models have gained considerable success for this task, while most existing approaches only focus on improving the informativeness of the summary, which ignore the correctness, i.e., the summary should not contain unrelated information with respect to the source sentence. We argue that correctness is an essential requirement for summarization systems. Considering a correct summary is semantically entailed by the source sentence, we incorporate entailment knowledge into abstractive summarization models. We propose an entailment-aware encoder under multi-task framework (i.e., summarization generation and entailment recognition) and an entailment-aware decoder by entailment Reward Augmented Maximum Likelihood (RAML) training. Experiment results demonstrate that our models significantly outperform baselines from the aspects of informativeness and correctness.
Fast and Accurate Reordering with ITG Transition RNN
Authors: Hao Zhang, Axel Ng, Richard Sproat
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Decoder, Feature Engineering, Machine Translation, Morphological Inflection, Speech Recognition, Text Normalization, Translation

Attention-based sequence-to-sequence neural network models learn to jointly align and translate. The quadratic-time attention mechanism is powerful as it is capable of handling arbitrary long-distance reordering, but computationally expensive. In this paper, towards making neural translation both accurate and efficient, we follow the traditional pre-reordering approach to decouple reordering from translation. We add a reordering RNN that shares the input encoder with the decoder. The RNNs are trained jointly with a multi-task loss function and applied sequentially at inference time. The task of the reordering model is to predict the permutation of the input words following the target language word order. After reordering, the attention in the decoder becomes more peaked and monotonic. For reordering, we adopt the Inversion Transduction Grammars (ITG) and propose a transition system to parse input to trees for reordering. We harness the ITG transition system with RNN. With the modeling power of RNN, we achieve superior reordering accuracy without any feature engineering. In experiments, we apply the model to the task of text normalization. Compared to a strong baseline of attention-based RNN, our ITG RNN re-ordering model can reach the same reordering accuracy with only 1/10 of the training data and is 2.5x faster in decoding.
Neural Machine Translation with Decoding History Enhanced Attention
Authors: Mingxuan Wang, Jun Xie, Zhixing Tan, Jinsong Su, Deyi Xiong, Chao Bian
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Language Modeling, Language Modelling, Machine Translation, NMT, Translation

Neural machine translation with source-side attention have achieved remarkable performance. however, there has been little work exploring to attend to the target-side which can potentially enhance the memory capbility of NMT. We reformulate a Decoding History Enhanced Attention mechanism (DHEA) to render NMT model better at selecting both source-side and target-side information. DHA enables dynamic control of the ratios at which source and target contexts contribute to the generation of target words, offering a way to weakly induce structure relations among both source and target tokens. It also allows training errors to be directly back-propagated through short-cut connections and effectively alleviates the gradient vanishing problem. The empirical study on Chinese-English translation shows that our model with proper configuration can improve by 0:9 BLEU upon Transformer and the best reported results in the dataset. On WMT14 English-German task and a larger WMT14 English-French task, our model achieves comparable results with the state-of-the-art.
Transfer Learning for a Letter-Ngrams to Word Decoder in the Context of Historical Handwriting Recognition with Scarce Resources
Authors: Adeline Granet, Emmanuel Morin, Harold Mouch{\`e}re, Solen Quiniou, Christian Viard-Gaudin
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Decoder, Handwriting Recognition, Information Retrieval, Keyword Spotting, Language Modeling, Language Modelling, Transfer Learning

Lack of data can be an issue when beginning a new study on historical handwritten documents. In order to deal with this, we present the character-based decoder part of a multilingual approach based on transductive transfer learning for a historical handwriting recognition task on Italian Comedy Registers. The decoder must build a sequence of characters that corresponds to a word from a vector of letter-ngrams. As learning data, we created a new dataset from untapped resources that covers the same domain and period of our Italian Comedy data, as well as resources from common domains, periods, or languages. We obtain a 97.42{\%} Character Recognition Rate and a 86.57{\%} Word Recognition Rate on our Italian Comedy data, despite a lexical coverage of 67{\%} between the Italian Comedy data and the training data. These results show that an efficient system can be obtained by a carefully selecting the datasets used for the transfer learning.
Crowdsourcing a Large Corpus of Clickbait on Twitter
Authors: Martin Potthast, Tim Gollub, Kristof Komlossy, Sebastian Schuster, Matti Wiegmann, Garces Fern, Erika Patricia ez, Matthias Hagen, Benno Stein
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Clickbait Detection

Clickbait has become a nuisance on social media. To address the urging task of clickbait detection, we constructed a new corpus of 38,517 annotated Twitter tweets, the Webis Clickbait Corpus 2017. To avoid biases in terms of publisher and topic, tweets were sampled from the top 27 most retweeted news publishers, covering a period of 150 days. Each tweet has been annotated on 4-point scale by five annotators recruited at Amazon{'}s Mechanical Turk. The corpus has been employed to evaluate 12 clickbait detectors submitted to the Clickbait Challenge 2017. Download: https://webis.de/data/webis-clickbait-17.html Challenge: https://clickbait-challenge.org
Cross-lingual Knowledge Projection Using Machine Translation and Target-side Knowledge Base Completion
Authors: Naoki Otani, Hirokazu Kiyomaru, Daisuke Kawahara, Sadao Kurohashi
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Knowledge Base Completion, Machine Translation, Translation

Considerable effort has been devoted to building commonsense knowledge bases. However, they are not available in many languages because the construction of KBs is expensive. To bridge the gap between languages, this paper addresses the problem of projecting the knowledge in English, a resource-rich language, into other languages, where the main challenge lies in projection ambiguity. This ambiguity is partially solved by machine translation and target-side knowledge base completion, but neither of them is adequately reliable by itself. We show their combination can project English commonsense knowledge into Japanese and Chinese with high precision. Our method also achieves a top-10 accuracy of 90{\%} on the crowdsourced English{--}Japanese benchmark. Furthermore, we use our method to obtain 18,747 facts of accurate Japanese commonsense within a very short period.
Assessing Quality Estimation Models for Sentence-Level Prediction
Authors: Hoang Cuong, Jia Xu
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Gaussian Processes, Machine Translation, Prediction, regression, Sentence

This paper provides an evaluation of a wide range of advanced sentence-level Quality Estimation models, including Support Vector Regression, Ride Regression, Neural Networks, Gaussian Processes, Bayesian Neural Networks, Deep Kernel Learning and Deep Gaussian Processes. Beside the accurateness, our main concerns are also the robustness of Quality Estimation models. Our work raises the difficulty in building strong models. Specifically, we show that Quality Estimation models often behave differently in Quality Estimation feature space, depending on whether the scale of feature space is small, medium or large. We also show that Quality Estimation models often behave differently in evaluation settings, depending on whether test data come from the same domain as the training data or not. Our work suggests several strong candidates to use in different circumstances.
User-Level Race and Ethnicity Predictors from Twitter Text
Authors: Daniel Preo{\c{t}}iuc-Pietro, Lyle Ungar
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

User demographic inference from social media text has the potential to improve a range of downstream applications, including real-time passive polling or quantifying demographic bias. This study focuses on developing models for user-level race and ethnicity prediction. We introduce a data set of users who self-report their race/ethnicity through a survey, in contrast to previous approaches that use distantly supervised data or perceived labels. We develop predictive models from text which accurately predict the membership of a user to the four largest racial and ethnic groups with up to .884 AUC and make these available to the research community.
Multi-Source Multi-Class Fake News Detection
Authors: Hamid Karimi, Proteek Roy, Sari Saba-Sadiya, Jiliang Tang
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Fake News Detection

Fake news spreading through media outlets poses a real threat to the trustworthiness of information and detecting fake news has attracted increasing attention in recent years. Fake news is typically written intentionally to mislead readers, which determines that fake news detection merely based on news content is tremendously challenging. Meanwhile, fake news could contain true evidence to mock true news and presents different degrees of fakeness, which further exacerbates the detection difficulty. On the other hand, the spread of fake news produces various types of data from different perspectives. These multiple sources provide rich contextual information about fake news and offer unprecedented opportunities for advanced fake news detection. In this paper, we study fake news detection with different degrees of fakeness by integrating multiple sources. In particular, we introduce approaches to combine information from multiple sources and to discriminate between different degrees of fakeness, and propose a Multi-source Multi-class Fake news Detection framework MMFD, which combines automated feature extraction, multi-source fusion and automated degrees of fakeness detection into a coherent and interpretable model. Experimental results on the real-world data demonstrate the effectiveness of the proposed framework and extensive experiments are further conducted to understand the working of the proposed framework.
Killing Four Birds with Two Stones: Multi-Task Learning for Non-Literal Language Detection
Authors: Erik-L{\^a}n Do Dinh, Steffen Eger, Iryna Gurevych
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: General Classification, Multi-Task Learning

Non-literal language phenomena such as idioms or metaphors are commonly studied in isolation from each other in NLP. However, often similar definitions and features are being used for different phenomena, challenging the distinction. Instead, we propose to view the detection problem as a generalized non-literal language classification problem. In this paper we investigate multi-task learning for related non-literal language phenomena. We show that in contrast to simply joining the data of multiple tasks, multi-task learning consistently improves upon four metaphor and idiom detection tasks in two languages, English and German. Comparing two state-of-the-art multi-task learning architectures, we also investigate when soft parameter sharing and learned information flow can be beneficial for our related tasks. We make our adapted code publicly available.
Twitter corpus of Resource-Scarce Languages for Sentiment Analysis and Multilingual Emoji Prediction
Authors: Nurendra Choudhary, Rajat Singh, Vijjini Anvesh Rao, Manish Shrivastava
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Sentiment Analysis

In this paper, we leverage social media platforms such as twitter for developing corpus across multiple languages. The corpus creation methodology is applicable for resource-scarce languages provided the speakers of that particular language are active users on social media platforms. We present an approach to extract social media microblogs such as tweets (Twitter). In this paper, we create corpus for multilingual sentiment analysis and emoji prediction in Hindi, Bengali and Telugu. Further, we perform and analyze multiple NLP tasks utilizing the corpus to get interesting observations.
Towards identifying the optimal datasize for lexically-based Bayesian inference of linguistic phylogenies
Authors: Taraka Rama, S{\o}ren Wichmann
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Bayesian Inference

Bayesian linguistic phylogenies are standardly based on cognate matrices for words referring to a fix set of meanings{---}typically around 100-200. To this day there has not been any empirical investigation into which datasize is optimal. Here we determine, across a set of language families, the optimal number of meanings required for the best performance in Bayesian phylogenetic inference. We rank meanings by stability, infer phylogenetic trees using first the most stable meaning, then the two most stable meanings, and so on, computing the quartet distance of the resulting tree to the tree proposed by language family experts at each step of datasize increase. When a gold standard tree is not available we propose to instead compute the quartet distance between the tree based on the n-most stable meaning and the one based on the n + 1-most stable meanings, increasing n from 1 to N âˆ’ 1, where N is the total number of meanings. The assumption here is that the value of n for which the quartet distance begins to stabilize is also the value at which the quality of the tree ceases to improve. We show that this assumption is borne out. The results of the two methods vary across families, and the optimal number of meanings appears to correlate with the number of languages under consideration.
Ab Initio: Automatic Latin Proto-word Reconstruction
Authors: Alina Maria Ciobanu, Liviu P. Dinu
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

Proto-word reconstruction is central to the study of language evolution. It consists of recreating the words in an ancient language from its modern daughter languages. In this paper we investigate automatic word form reconstruction for Latin proto-words. Having modern word forms in multiple Romance languages (French, Italian, Spanish, Portuguese and Romanian), we infer the form of their common Latin ancestors. Our approach relies on the regularities that occurred when the Latin words entered the modern languages. We leverage information from all modern languages, building an ensemble system for proto-word reconstruction. We use conditional random fields for sequence labeling, but we conduct preliminary experiments with recurrent neural networks as well. We apply our method on multiple datasets, showing that our method improves on previous results, having also the advantage of requiring less input data, which is essential in historical linguistics, where resources are generally scarce.
A Computational Model for the Linguistic Notion of Morphological Paradigm
Authors: Miikka Silfverberg, Ling Liu, Mans Hulden
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

In supervised learning of morphological patterns, the strategy of generalizing inflectional tables into more abstract paradigms through alignment of the longest common subsequence found in an inflection table has been proposed as an efficient method to deduce the inflectional behavior of unseen word forms. In this paper, we extend this notion of morphological {`}paradigm{'} from earlier work and provide a formalization that more accurately matches linguist intuitions about what an inflectional paradigm is. Additionally, we propose and evaluate a mechanism for learning full human-readable paradigm specifications from incomplete data{---}a scenario when we only have access to a few inflected forms for each lexeme, and want to reconstruct the missing inflections as well as generalize and group the witnessed patterns into a model of more abstract paradigmatic behavior of lexemes.
Relation Induction in Word Embeddings Revisited
Authors: Zied Bouraoui, Shoaib Jameel, Steven Schockaert
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Knowledge Base Completion, regression, Relation, Translation, Word Embeddings

Given a set of instances of some relation, the relation induction task is to predict which other word pairs are likely to be related in the same way. While it is natural to use word embeddings for this task, standard approaches based on vector translations turn out to perform poorly. To address this issue, we propose two probabilistic relation induction models. The first model is based on translations, but uses Gaussians to explicitly model the variability of these translations and to encode soft constraints on the source and target words that may be chosen. In the second model, we use Bayesian linear regression to encode the assumption that there is a linear relationship between the vector representations of related words, which is considerably weaker than the assumption underlying translation based models.