pwc-archive/papers-with-abstracts huggingface dataset:
Identifying Linguistic Areas for Geolocation
Authors: Tommaso Fornaciari, Dirk Hovy
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Clustering

Geolocating social media posts relies on the assumption that language carries sufficient geographic information. However, locations are usually given as continuous latitude/longitude tuples, so we first need to define discrete geographic regions that can serve as labels. Most studies use some form of clustering to discretize the continuous coordinates (Han et al., 2016). However, the resulting regions do not always correspond to existing linguistic areas. Consequently, accuracy at 100 miles tends to be good, but degrades for finer-grained distinctions, when different linguistic regions get lumped together. We describe a new algorithm, Point-to-City (P2C), an iterative k-d tree-based method for clustering geographic coordinates and associating them with towns. We create three sets of labels at different levels of granularity, and compare performance of a state-of-the-art geolocation model trained and tested with P2C labels to one with regular k-d tree labels. Even though P2C results in substantially more labels than the baseline, model accuracy increases significantly over using traditional labels at the fine-grained level, while staying comparable at 100 miles. The results suggest that identifying meaningful linguistic areas is crucial for improving geolocation at a fine-grained level.
Distant Supervised Relation Extraction with Separate Head-Tail CNN
Authors: Rui Xing, Jie Luo
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Relation, Relation Extraction

Distant supervised relation extraction is an efficient and effective strategy to find relations between entities in texts. However, it inevitably suffers from mislabeling problem and the noisy data will hinder the performance. In this paper, we propose the Separate Head-Tail Convolution Neural Network (SHTCNN), a novel neural relation extraction framework to alleviate this issue. In this method, we apply separate convolution and pooling to the head and tail entity respectively for extracting better semantic features of sentences, and coarse-to-fine strategy to filter out instances which do not have actual relations in order to alleviate noisy data issues. Experiments on a widely used dataset show that our model achieves significant and consistent improvements in relation extraction compared to statistical and vanilla CNN-based methods.
Discovering the Functions of Language in Online Forums
Authors: Youmna Ismaeil, Oana Balalau, Paramita Mirza
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11

In this work, we revisit the functions of language proposed by linguist Roman Jakobson and we highlight their potential in analyzing online forum conversations. We investigate the relationship between functions and other properties of comments, such as controversiality. We propose and evaluate a semi-supervised framework for predicting the functions of Reddit comments. To accommodate further research, we release a corpus of 165K comments annotated with their functions of language.
Benefits of Data Augmentation for NMT-based Text Normalization of User-Generated Content
Authors: Claudia Matos Veliz, Orphee De Clercq, Veronique Hoste
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Data Augmentation, Decoder, Machine Translation, NMT, Sentence, Text Normalization

One of the most persistent characteristics of written user-generated content (UGC) is the use of non-standard words. This characteristic contributes to an increased difficulty to automatically process and analyze UGC. Text normalization is the task of transforming lexical variants to their canonical forms and is often used as a pre-processing step for conventional NLP tasks in order to overcome the performance drop that NLP systems experience when applied to UGC. In this work, we follow a Neural Machine Translation approach to text normalization. To train such an encoder-decoder model, large parallel training corpora of sentence pairs are required. However, obtaining large data sets with UGC and their normalized version is not trivial, especially for languages other than English. In this paper, we explore how to overcome this data bottleneck for Dutch, a low-resource language. We start off with a small publicly available parallel Dutch data set comprising three UGC genres and compare two different approaches. The first is to manually normalize and add training data, a money and time-consuming task. The second approach is a set of data augmentation techniques which increase data size by converting existing resources into synthesized non-standard forms. Our results reveal that, while the different approaches yield similar results regarding the normalization issues in the test set, they also introduce a large amount of over-normalizations.
Contextual Text Denoising with Masked Language Model
Authors: Yifu Sun, Haoming Jiang
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Denoising, Language Modeling, Language Modelling, model

Recently, with the help of deep learning models, significant advances have been made in different Natural Language Processing (NLP) tasks. Unfortunately, state-of-the-art models are vulnerable to noisy texts. We propose a new contextual text denoising algorithm based on the ready-to-use masked language model. The proposed algorithm does not require retraining of the model and can be integrated into any NLP system without additional training on paired cleaning training data. We evaluate our method under synthetic noise and natural noise and show that the proposed algorithm can use context information to correct noise text and improve the performance of noisy inputs in several downstream tasks.
Towards Automated Semantic Role Labelling of Hindi-English Code-Mixed Tweets
Authors: Riya Pal, Dipti Sharma
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11

We present a system for automating Semantic Role Labelling of Hindi-English code-mixed tweets. We explore the issues posed by noisy, user generated code-mixed social media data. We also compare the individual effect of various linguistic features used in our system. Our proposed model is a 2-step system for automated labelling which gives an overall accuracy of 84{\%} for Argument Classification, marking a 10{\%} increase over the existing rule-based baseline model. This is the first attempt at building a statistical Semantic Role Labeller for Hindi-English code-mixed data, to the best of our knowledge.
Enhancing BERT for Lexical Normalization
Authors: Benjamin Muller, Benoit Sagot, Djam{\'e} Seddah
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Language Modeling, Language Modelling, Lexical Normalization

Language model-based pre-trained representations have become ubiquitous in natural language processing. They have been shown to significantly improve the performance of neural models on a great variety of tasks. However, it remains unclear how useful those general models can be in handling non-canonical text. In this article, focusing on User Generated Content (UGC), we study the ability of BERT to perform lexical normalisation. Our contribution is simple: by framing lexical normalisation as a token prediction task, by enhancing its architecture and by carefully fine-tuning it, we show that BERT can be a competitive lexical normalisation model without the need of any UGC resources aside from 3,000 training sentences. To the best of our knowledge, it is the first work done in adapting and analysing the ability of this model to handle noisy UGC data.
No, you're not alone: A better way to find people with similar experiences on Reddit
Authors: Zhilin Wang, Elena Rastorgueva, Weizhe Lin, Xiaodong Wu
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Clustering, Sentence

We present a probabilistic clustering algorithm that can help Reddit users to find posts that discuss experiences similar to their own. This model is built upon the BERT Next Sentence Prediction model and reduces the time complexity for clustering all posts in a corpus from O(n{\^{}}2) to O(n) with respect to the number of posts. We demonstrate that such probabilistic clustering can yield a performance better than baseline clustering methods based on Latent Dirichlet Allocation (Blei et al., 2003) and Word2Vec (Mikolov et al., 2013). Furthermore, there is a high degree of coherence between our probabilistic clustering and the exhaustive comparison O(n{\^{}}2) algorithm in which the similarity between every pair of posts is found. This makes the use of the BERT Next Sentence Prediction model more practical for unsupervised clustering tasks due to the high runtime overhead of each BERT computation.
Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge
Authors: Wenhao Ying, Rong Xiang, Qin Lu
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Emotion Classification, General Classification, General Knowledge, Language Modeling, Language Modelling, Sentiment Analysis

Deep learning based general language models have achieved state-of-the-art results in many popular tasks such as sentiment analysis and QA tasks. Text in domains like social media has its own salient characteristics. Domain knowledge should be helpful in domain relevant tasks. In this work, we devise a simple method to obtain domain knowledge and further propose a method to integrate domain knowledge with general knowledge based on deep language models to improve performance of emotion classification. Experiments on Twitter data show that even though a deep language model fine-tuned by a target domain data has attained comparable results to that of previous state-of-the-art models, this fine-tuned model can still benefit from our extracted domain knowledge to obtain more improvement. This highlights the importance of making use of domain knowledge in domain-specific applications.
Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back-Translation
Authors: Zhenhao Li, Lucia Specia
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Data Augmentation, Diversity, Domain Adaptation, Form, Machine Translation, NMT, Translation

Neural Machine Translation (NMT) models have been proved strong when translating clean texts, but they are very sensitive to noise in the input. Improving NMT models robustness can be seen as a form of {``}domain{''} adaption to noise. The recently created Machine Translation on Noisy Text task corpus provides noisy-clean parallel data for a few language pairs, but this data is very limited in size and diversity. The state-of-the-art approaches are heavily dependent on large volumes of back-translated data. This paper has two main contributions: Firstly, we propose new data augmentation methods to extend limited noisy data and further improve NMT robustness to noise while keeping the models small. Secondly, we explore the effect of utilizing noise from external data in the form of speech transcripts and show that it could help robustness.
An Ensemble of Humour, Sarcasm, and Hate Speechfor Sentiment Classification in Online Reviews
Authors: Rohan Badlani, Nishit Asnani, Manan Rai
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: General Classification, Sentiment Analysis, Sentiment Classification

Due to the nature of online user reviews, sentiment analysis on such data requires a deep semantic understanding of the text. Many online reviews are sarcastic, humorous, or hateful. Signals from such language nuances may reinforce or completely alter the sentiment of a review as predicted by a machine learning model that attempts to detect sentiment alone. Thus, having a model that is explicitly aware of these features should help it perform better on reviews that are characterized by them. We propose a composite two-step model that extracts features pertaining to sarcasm, humour, hate speech, as well as sentiment, in the first step, feeding them in conjunction to inform sentiment classification in the second step. We show that this multi-step approach leads to a better empirical performance for sentiment classification than a model that predicts sentiment alone. A qualitative analysis reveals that the conjunctive approach can better capture the nuances of sentiment as expressed in online reviews.
Minimally-Augmented Grammatical Error Correction
Authors: Roman Grundkiewicz, Marcin Junczys-Dowmunt
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Grammatical Error Correction

There has been an increased interest in low-resource approaches to automatic grammatical error correction. We introduce Minimally-Augmented Grammatical Error Correction (MAGEC) that does not require any error-labelled data. Our unsupervised approach is based on a simple but effective synthetic error generation method based on confusion sets from inverted spell-checkers. In low-resource settings, we outperform the current state-of-the-art results for German and Russian GEC tasks by a large margin without using any real error-annotated training data. When combined with labelled data, our method can serve as an efficient pre-training technique
A Social Opinion Gold Standard for the Malta Government Budget 2018
Authors: Keith Cortis, Brian Davis
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Negation, Opinion Mining

We present a gold standard of annotated social opinion for the Malta Government Budget 2018. It consists of over 500 online posts in English and/or the Maltese less-resourced language, gathered from social media platforms, specifically, social networking services and newswires, which have been annotated with information about opinions expressed by the general public and other entities, in terms of sentiment polarity, emotion, sarcasm/irony, and negation. This dataset is a resource for opinion mining based on social data, within the context of politics. It is the first opinion annotated social dataset from Malta, which has very limited language resources available.
The Fallacy of Echo Chambers: Analyzing the Political Slants of User-Generated News Comments in Korean Media
Authors: Jiyoung Han, Youngin Lee, Junbum Lee, Meeyoung Cha
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11

This study analyzes the political slants of user comments on Korean partisan media. We built a BERT-based classifier to detect political leaning of short comments via the use of semi-unsupervised deep learning methods that produced an F1 score of 0.83. As a result of classifying 21.6K comments, we found the high presence of conservative bias on both conservative and liberal news outlets. Moreover, this study discloses an asymmetry across the partisan spectrum in that more liberals (48.0{\%}) than conservatives (23.6{\%}) comment not only on news stories resonating with their political perspectives but also on those challenging their viewpoints. These findings advance the current understanding of online echo chambers.
Y'all should read this! Identifying Plurality in Second-Person Personal Pronouns in English Texts
Authors: Gabriel Stanovsky, Ronen Tamari
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: coreference-resolution, Coreference Resolution, Machine Translation, Translation

Distinguishing between singular and plural {``}you{''} in English is a challenging task which has potential for downstream applications, such as machine translation or coreference resolution. While formal written English does not distinguish between these cases, other languages (such as Spanish), as well as other dialects of English (via phrases such as {``}y{'}all{''}), do make this distinction. We make use of this to obtain distantly-supervised labels for the task on a large-scale in two domains. Following, we train a model to distinguish between the single/plural {`}you{'}, finding that although in-domain training achieves reasonable accuracy ({\mbox{$\geq$}} 77{\%}), there is still a lot of room for improvement, especially in the domain-transfer scenario, which proves extremely challenging. Our code and data are publicly available.
Additive Compositionality of Word Vectors
Authors: Yeon Seonwoo, Sungjoon Park, Dongkwan Kim, Alice Oh
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Sentence, Sentence Similarity, Word Similarity

Additive compositionality of word embedding models has been studied from empirical and theoretical perspectives. Existing research on justifying additive compositionality of existing word embedding models requires a rather strong assumption of uniform word distribution. In this paper, we relax that assumption and propose more realistic conditions for proving additive compositionality, and we develop a novel word and sub-word embedding model that satisfies additive compositionality under those conditions. We then empirically show our model{'}s improved semantic representation performance on word similarity and noisy sentence similarity.
Contextualized context2vec
Authors: Kazuki Ashihara, Tomoyuki Kajiwara, Yuki Arase, Satoru Uchida
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Sentence, Word Embeddings

Lexical substitution ranks substitution candidates from the viewpoint of paraphrasability for a target word in a given sentence. There are two major approaches for lexical substitution: (1) generating contextualized word embeddings by assigning multiple embeddings to one word and (2) generating context embeddings using the sentence. Herein we propose a method that combines these two approaches to contextualize word embeddings for lexical substitution. Experiments demonstrate that our method outperforms the current state-of-the-art method. We also create CEFR-LP, a new evaluation dataset for the lexical substitution task. It has a wider coverage of substitution candidates than previous datasets and assigns English proficiency levels to all target words and substitution candidates.
Phonetic Normalization for Machine Translation of User Generated Content
Authors: Jos{\'e} Carlos Rosales N{\'u}{\~n}ez, Djam{\'e} Seddah, Guillaume Wisniewski
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

We present an approach to correct noisy User Generated Content (UGC) in French aiming to produce a pretreatement pipeline to improve Machine Translation for this kind of non-canonical corpora. In order to do so, we have implemented a character-based neural model phonetizer to produce IPA pronunciations of words. In this way, we intend to correct grammar, vocabulary and accentuation errors often present in noisy UGC corpora. Our method leverages on the fact that some errors are due to confusion induced by words with similar pronunciation which can be corrected using a phonetic look-up table to produce normalization candidates. These potential corrections are then encoded in a lattice and ranked using a language model to output the most probable corrected phrase. Compare to using other phonetizers, our method boosts a transformer-based machine translation system on UGC.
Normalization of Indonesian-English Code-Mixed Twitter Data
Authors: Anab Maulana Barik, Rahmad Mahendra, Mirna Adriani
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Language Identification, Lexical Normalization, Translation

Twitter is an excellent source of data for NLP researches as it offers tremendous amount of textual data. However, processing tweet to extract meaningful information is very challenging, at least for two reasons: (i) using nonstandard words as well as informal writing manner, and (ii) code-mixing issues, which is combining multiple languages in single tweet conversation. Most of the previous works have addressed both issues in isolated different task. In this study, we work on normalization task in code-mixed Twitter data, more specifically in Indonesian-English language. We propose a pipeline that consists of four modules, i.e tokenization, language identification, lexical normalization, and translation. Another contribution is to provide a gold standard of Indonesian-English code-mixed data for each module.
Unsupervised Neologism Normalization Using Embedding Space Mapping
Authors: Nasser Zalmout, Kapil Thadani, Aasish Pappu
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Natural Language Understanding, Text Normalization

This paper presents an approach for detecting and normalizing neologisms in social media content. Neologisms refer to recent expressions that are specific to certain entities or events and are being increasingly used by the public, but have not yet been accepted in mainstream language. Automated methods for handling neologisms are important for natural language understanding and normalization, especially for informal genres with user generated content. We present an unsupervised approach for detecting neologisms and then normalizing them to canonical words without relying on parallel training data. Our approach builds on the text normalization literature and introduces adaptations to fit the specificities of this task, including phonetic and etymological considerations. We evaluate the proposed techniques on a dataset of Reddit comments, with detected neologisms and corresponding normalizations.
Towards Actual (Not Operational) Textual Style Transfer Auto-Evaluation
Authors: Richard Yuanzhe Pang
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Semantic Similarity, Semantic Textual Similarity, Style Transfer

Regarding the problem of automatically generating paraphrases with modified styles or attributes, the difficulty lies in the lack of parallel corpora. Numerous advances have been proposed for the generation. However, significant problems remain with the auto-evaluation of style transfer tasks. Based on the summary of Pang and Gimpel (2018) and Mir et al. (2019), style transfer evaluations rely on three metrics: post-transfer style classification accuracy, content or semantic similarity, and naturalness or fluency. We elucidate the dangerous current state of style transfer auto-evaluation research. Moreover, we propose ways to aggregate the three metrics into one evaluator. This abstract aims to bring researchers to think about the future of style transfer and style transfer evaluation research.
Hello, It's GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems
Authors: Pawe{\l} Budzianowski, Ivan Vuli{\'c}
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Decision Making, Language Modeling, Language Modelling, Task-Oriented Dialogue Systems, Text Generation, Transfer Learning

Data scarcity is a long-standing and crucial challenge that hinders quick development of task-oriented dialogue systems across multiple domains: task-oriented dialogue models are expected to learn grammar, syntax, dialogue reasoning, decision making, and language generation from absurdly small amounts of task-specific data. In this paper, we demonstrate that recent progress in language modeling pre-training and transfer learning shows promise to overcome this problem. We propose a task-oriented dialogue model that operates solely on text input: it effectively bypasses explicit policy and language generation modules. Building on top of the TransferTransfo framework (Wolf et al., 2019) and generative model pre-training (Radford et al., 2019), we validate the approach on complex multi-domain task-oriented dialogues from the MultiWOZ dataset. Our automatic and human evaluations show that the proposed model is on par with a strong task-specific neural baseline. In the long run, our approach holds promise to mitigate the data scarcity problem, and to support the construction of more engaging and more eloquent task-oriented conversational agents.