pwc-archive/papers-with-abstracts huggingface dataset:
Using Eye-tracking Data to Predict the Readability of Brazilian Portuguese Sentences in Single-task, Multi-task and Sequential Transfer Learning Approaches
Authors: Sidney Evaldo Leal, Jo{\~a}o Marcos Munguba Vieira, Erica dos Santos Rodrigues, Elis{\^a}ngela Nogueira Teixeira, Sandra Alu{\'\i}sio
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Multi-Task Learning, Sentence, Transfer Learning

Sentence complexity assessment is a relatively new task in Natural Language Processing. One of its aims is to highlight in a text which sentences are more complex to support the simplification of contents for a target audience (e.g., children, cognitively impaired users, non-native speakers and low-literacy readers (Scarton and Specia, 2018)). This task is evaluated using datasets of pairs of aligned sentences including the complex and simple version of the same sentence. For Brazilian Portuguese, the task was addressed by (Leal et al., 2018), who set up the first dataset to evaluate the task in this language, reaching 87.8{\%} of accuracy with linguistic features. The present work advances these results, using models inspired by (Gonzalez-Gardu{\~n}o and S{\o}gaard, 2018), which hold the state-of-the-art for the English language, with multi-task learning and eye-tracking measures. First-Pass Duration, Total Regression Duration and Total Fixation Duration were used in two moments; first to select a subset of linguistic features and then as an auxiliary task in the multi-task and sequential learning models. The best model proposed here reaches the new state-of-the-art for Portuguese with 97.5{\%} accuracy 1 , an increase of almost 10 points compared to the best previous results, in addition to proposing improvements in the public dataset after analysing the errors of our best model.
Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification Framework
Authors: Akshay Bhola, Kishaloy Halder, Animesh Prasad, Min-Yen Kan
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Extreme Multi-Label Classification, Language Modeling, Language Modelling, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

We introduce a deep learning model to learn the set of enumerated job skills associated with a job description. In our analysis of a large-scale government job portal mycareersfuture.sg, we observe that as much as 65{\%} of job descriptions miss describing a significant number of relevant skills. Our model addresses this task from the perspective of an extreme multi-label classification (XMLC) problem, where descriptions are the evidence for the binary relevance of thousands of individual skills. Building upon the current state-of-the-art language modeling approaches such as BERT, we show our XMLC method improves on an existing baseline solution by over 9{\%} and 7{\%} absolute improvements in terms of recall and normalized discounted cumulative gain. We further show that our approach effectively addresses the missing skills problem, and helps in recovering relevant skills that were missed out in the job postings by taking into account the structured semantic representation of skills and their co-occurrences through a Correlation Aware Bootstrapping process. We further show that our approach, to ensure the BERT-XMLC model accounts for structured semantic representation of skills and their co-occurrences through a Correlation Aware Bootstrapping process, effectively addresses the missing skills problem, and helps in recovering relevant skills that were missed out in the job postings. To facilitate future research and replication of our work, we have made the dataset and the implementation of our model publicly available.
Native-like Expression Identification by Contrasting Native and Proficient Second Language Speakers
Authors: Oleksandr Harust, Yugo Murawaki, Sadao Kurohashi
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Sentence

We propose a novel task of native-like expression identification by contrasting texts written by native speakers and those by proficient second language speakers. This task is highly challenging mainly because 1) the combinatorial nature of expressions prevents us from choosing candidate expressions a priori and 2) the distributions of the two types of texts overlap considerably. Our solution to the first problem is to combine a powerful neural network-based classifier of sentence-level nativeness with an explainability method that measures an approximate contribution of a given expression to the classifier{'}s prediction. To address the second problem, we introduce a special label neutral and reformulate the classification task as complementary-label learning. Our crowdsourcing-based evaluation and in-depth analysis suggest that our method successfully uncovers linguistically interesting usages distinctive of native speech.
The Indigenous Languages Technology project at NRC Canada: An empowerment-oriented approach to developing language software
Authors: Roland Kuhn, Fineen Davis, Alain D{\'e}silets, Eric Joanis, Anna Kazantseva, Rebecca Knowles, Patrick Littell, Delaney Lothian, Aidan Pine, Caroline Running Wolf, Eddie Santos, Darlene Stewart, Gilles Boulianne, Vishwa Gupta, Brian Maracle Owennat{\'e}kha, Akwirat{\'e}kha{'} Martin, Christopher Cox, Marie-Odile Junker, Olivia Sammons, Delasie Torkornoo, Nathan Thanyeht{\'e}nhas Brinklow, Sara Child, Beno{\^\i}t Farley, David Huggins-Daines, Daisy Rosenblum, Heather Souter
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, speech-recognition, Speech Recognition, Translation

This paper surveys the first, three-year phase of a project at the National Research Council of Canada that is developing software to assist Indigenous communities in Canada in preserving their languages and extending their use. The project aimed to work within the empowerment paradigm, where collaboration with communities and fulfillment of their goals is central. Since many of the technologies we developed were in response to community needs, the project ended up as a collection of diverse subprojects, including the creation of a sophisticated framework for building verb conjugators for highly inflectional polysynthetic languages (such as Kanyen{'}k{\'e}ha, in the Iroquoian language family), release of what is probably the largest available corpus of sentences in a polysynthetic language (Inuktut) aligned with English sentences and experiments with machine translation (MT) systems trained on this corpus, free online services based on automatic speech recognition (ASR) for easing the transcription bottleneck for recordings of speech in Indigenous languages (and other languages), software for implementing text prediction and read-along audiobooks for Indigenous languages, and several other subprojects.
Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings
Authors: Arun Ramachandran, Gerard de Melo
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Sentence, Translation

Emotion lexicons provide information about associations between words and emotions. They have proven useful in analyses of reviews, literary texts, and posts on social media, among other things. We evaluate the feasibility of deriving emotion lexicons cross-lingually, especially for low-resource languages, from existing emotion lexicons in resource-rich languages. For this, we start out from very small corpora to induce cross-lingually aligned vector spaces. Our study empirically analyses the effectiveness of the induced emotion lexicons by measuring translation precision and correlations with existing emotion lexicons, along with measurements on a downstream task of sentence emotion prediction.
100,000 Podcasts: A Spoken English Document Corpus
Authors: Ann Clifton, Sravana Reddy, Yongze Yu, Aasish Pappu, Rezvaneh Rezapour, Hamed Bonab, Maria Eskevich, Gareth Jones, Jussi Karlgren, Ben Carterette, Rosie Jones
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: 3D Facial Landmark Localization, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Facial Expression Recognition (FER), Highlight Detection, Information Retrieval, Retrieval, speech-recognition, Speech Recognition

Podcasts are a large and growing repository of spoken audio. As an audio format, podcasts are more varied in style and production type than broadcast news, contain more genres than typically studied in video data, and are more varied in style and format than previous corpora of conversations. When transcribed with automatic speech recognition they represent a noisy but fascinating collection of documents which can be studied through the lens of natural language processing, information retrieval, and linguistics. Paired with the audio files, they are also a resource for speech processing and the study of paralinguistic, sociolinguistic, and acoustic aspects of the domain. We introduce the Spotify Podcast Dataset, a new corpus of 100,000 podcasts. We demonstrate the complexity of the domain with a case study of two tasks: (1) passage search and (2) summarization. This is orders of magnitude larger than previous speech corpora used for search and summarization. Our results show that the size and variability of this corpus opens up new avenues for research.
A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment
Authors: Zhiwen Xie, Runjie Zhu, Kunsong Zhao, Jin Liu, Guangyou Zhou, Jimmy Xiangji Huang
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Entity Alignment, Graph Attention, Graph Neural Network

Cross-lingual entity alignment, which aims to match equivalent entities in KGs with different languages, has attracted considerable focus in recent years. Recently, many graph neural network (GNN) based methods are proposed for entity alignment and obtain promising results. However, existing GNN-based methods consider the two KGs independently and learn embeddings for different KGs separately, which ignore the useful pre-aligned links between two KGs. In this paper, we propose a novel Contextual Alignment Enhanced Cross Graph Attention Network (CAECGAT) for the task of cross-lingual entity alignment, which is able to jointly learn the embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments. We conduct extensive experiments on three benchmark cross-lingual entity alignment datasets. The experimental results demonstrate that our proposed method obtains remarkable performance gains compared to state-of-the-art methods.
Detecting Non-literal Translations by Fine-tuning Cross-lingual Pre-trained Language Models
Authors: Yuming Zhai, Gabriel Illouz, Anne Vilnat
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Language Modelling, Sentence, Translation

Human-generated non-literal translations reflect the richness of human languages and are sometimes indispensable to ensure adequacy and fluency. Non-literal translations are difficult to produce even for human translators, especially for foreign language learners, and machine translations are still on the way to simulate human ones on this aspect. In order to foster the study on appropriate and creative non-literal translations, automatically detecting them in parallel corpora is an important step, which can benefit downstream NLP tasks or help to construct materials to teach translation. This article demonstrates that generic sentence representations produced by a pre-trained cross-lingual language model could be fine-tuned to solve this task. We show that there exists a moderate positive correlation between the prediction probability of being human translation and the non-literal translations{'} proportion in a sentence. The fine-tuning experiments show an accuracy of 80.16{\%} when predicting the presence of non-literal translations in a sentence and an accuracy of 85.20{\%} when distinguishing literal and non-literal translations at phrase level. We further conduct a linguistic error analysis and propose directions for future work.
Data Selection for Bilingual Lexicon Induction from Specialized Comparable Corpora
Authors: Martin Laville, Amir Hazem, Emmanuel Morin, Phillippe Langlais
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Bilingual Lexicon Induction, Data Augmentation, Machine Translation, Translation

Narrow specialized comparable corpora are often small in size. This particularity makes it difficult to build efficient models to acquire translation equivalents, especially for less frequent and rare words. One way to overcome this issue is to enrich the specialized corpora with out-of-domain resources. Although some recent studies have shown improvements using data augmentation, the enrichment method was roughly conducted by adding out-of-domain data with no particular attention given to how to enrich words and how to do it optimally. In this paper, we contrast several data selection techniques to improve bilingual lexicon induction from specialized comparable corpora. We first apply two well-established data selection techniques often used in machine translation that is: Tf-Idf and cross entropy. Then, we propose to exploit BERT for data selection. Overall, all the proposed techniques improve the quality of the extracted bilingual lexicons by a large margin. The best performing model is the cross entropy, obtaining a gain of about 4 points in MAP while decreasing computation time by a factor of 10.
A Locally Linear Procedure for Word Translation
Authors: Soham Dan, Hagai Taitelbaum, Jacob Goldberger
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Translation, Word Embeddings, Word Similarity, Word Translation

Learning a mapping between word embeddings of two languages given a dictionary is an important problem with several applications. A common mapping approach is using an orthogonal matrix. The Orthogonal Procrustes Analysis (PA) algorithm can be applied to find the optimal orthogonal matrix. This solution restricts the expressiveness of the translation model which may result in sub-optimal translations. We propose a natural extension of the PA algorithm that uses multiple orthogonal translation matrices to model the mapping and derive an algorithm to learn these multiple matrices. We achieve better performance in a bilingual word translation task and a cross-lingual word similarity task compared to the single matrix baseline. We also show how multiple matrices can model multiple senses of a word.
The SADID Evaluation Datasets for Low-Resource Spoken Language Machine Translation of Arabic Dialects
Authors: Wael Abid
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Machine Translation, Translation

Low-resource Machine Translation recently gained a lot of popularity, and for certain languages, it has made great strides. However, it is still difficult to track progress in other languages for which there is no publicly available evaluation data. In this paper, we introduce benchmark datasets for Arabic and its dialects. We describe our design process and motivations and analyze the datasets to understand their resulting properties. Numerous successful attempts use large monolingual corpora to augment low-resource pairs. We try to approach augmentation differently and investigate whether it is possible to improve MT models without any external sources of data. We accomplish this by bootstrapping existing parallel sentences and complement this with multilingual training to achieve strong baselines.
Combining Word Embeddings with Bilingual Orthography Embeddings for Bilingual Dictionary Induction
Authors: Silvia Severini, Viktor Hangya, Alexander Fraser, Hinrich Sch{\"u}tze
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Translation, Transliteration, Word Embeddings

Bilingual dictionary induction (BDI) is the task of accurately translating words to the target language. It is of great importance in many low-resource scenarios where cross-lingual training data is not available. To perform BDI, bilingual word embeddings (BWEs) are often used due to their low bilingual training signal requirements. They achieve high performance, but problematic cases still remain, such as the translation of rare words or named entities, which often need to be transliterated. In this paper, we enrich BWE-based BDI with transliteration information by using Bilingual Orthography Embeddings (BOEs). BOEs represent source and target language transliteration word pairs with similar vectors. A key problem in our BDI setup is to decide which information source {--} BWEs (or semantics) vs. BOEs (or orthography) {--} is more reliable for a particular word pair. We propose a novel classification-based BDI system that uses BWEs, BOEs and a number of other features to make this decision. We test our system on English-Russian BDI and show improved performance. In addition, we show the effectiveness of our BOEs by successfully using them for transliteration mining based on cosine similarity.
Understanding Translationese in Multi-view Embedding Spaces
Authors: Koel Dutta Chowdhury, Cristina Espa{\~n}a-Bonet, Josef van Genabith
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Translation

Recent studies use a combination of lexical and syntactic features to show that footprints of the source language remain visible in translations, to the extent that it is possible to predict the original source language from the translation. In this paper, we focus on embedding-based semantic spaces, exploiting departures from isomorphism between spaces built from original target language and translations into this target language to predict relations between languages in an unsupervised way. We use different views of the data {---} words, parts of speech, semantic tags and synsets {---} to track translationese. Our analysis shows that (i) semantic distances between original target language and translations into this target language can be detected using the notion of isomorphism, (ii) language family ties with characteristics similar to linguistically motivated phylogenetic trees can be inferred from the distances and (iii) with delexicalised embeddings exhibiting source-language interference most significantly, other levels of abstraction display the same tendency, indicating the lexicalised results to be not {``}just{''} due to possible topic differences between original and translated texts. To the best of our knowledge, this is the first time departures from isomorphism between embedding spaces are used to track translationese.
Building The First English-Brazilian Portuguese Corpus for Automatic Post-Editing
Authors: Felipe Almeida Costa, Thiago castro Ferreira, Adriana Pagano, Wagner Meira
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Automatic Post-Editing, Machine Translation, Translation

This paper introduces the first corpus for Automatic Post-Editing of English and a low-resource language, Brazilian Portuguese. The source English texts were extracted from the WebNLG corpus and automatically translated into Portuguese using a state-of-the-art industrial neural machine translator. Post-edits were then obtained in an experiment with native speakers of Brazilian Portuguese. To assess the quality of the corpus, we performed error analysis and computed complexity indicators measuring how difficult the APE task would be. We report preliminary results of Phrase-Based and Neural Machine Translation Models on this new corpus. Data and code publicly available in our repository.
Analysing cross-lingual transfer in lemmatisation for Indian languages
Authors: Kumar Saurav, Kumar Saunack, Pushpak Bhattacharyya
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Cross-Lingual Transfer, Lemmatization

Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. However, most of the prior work on this topic has focused on high resource languages. In this paper, we evaluate cross-lingual approaches for low resource languages, especially in the context of morphologically rich Indian languages. We test our model on six languages from two different families and develop linguistic insights into each model{'}s performance.
Neural Automated Essay Scoring Incorporating Handcrafted Features
Authors: Masaki Uto, Yikuan Xie, Maomi Ueno
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Automated Essay Scoring, Feature Engineering, Sentence

Automated essay scoring (AES) is the task of automatically assigning scores to essays as an alternative to grading by human raters. Conventional AES typically relies on handcrafted features, whereas recent studies have proposed AES models based on deep neural networks (DNNs) to obviate the need for feature engineering. Furthermore, hybrid methods that integrate handcrafted features in a DNN-AES model have been recently developed and have achieved state-of-the-art accuracy. One of the most popular hybrid methods is formulated as a DNN-AES model with an additional recurrent neural network (RNN) that processes a sequence of handcrafted sentence-level features. However, this method has the following problems: 1) It cannot incorporate effective essay-level features developed in previous AES research. 2) It greatly increases the numbers of model parameters and tuning parameters, increasing the difficulty of model training. 3) It has an additional RNN to process sentence-level features, enabling extension to various DNN-AES models complex. To resolve these problems, we propose a new hybrid method that integrates handcrafted essay-level features into a DNN-AES model. Specifically, our method concatenates handcrafted essay-level features to a distributed essay representation vector, which is obtained from an intermediate layer of a DNN-AES model. Our method is a simple DNN-AES extension, but significantly improves scoring accuracy.
A Straightforward Approach to Narratologically Grounded Character Identification
Authors: Labiba Jahan, Rahul Mittal, W. Victor Yarlott, Mark Finlayson
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Natural Language Understanding

One of the most fundamental elements of narrative is character: if we are to understand a narrative, we must be able to identify the characters of that narrative. Therefore, character identification is a critical task in narrative natural language understanding. Most prior work has lacked a narratologically grounded definition of character, instead relying on simplified or implicit definitions that do not capture essential distinctions between characters and other referents in narratives. In prior work we proposed a preliminary definition of character that was based in clear narratological principles: a character is an animate entity that is important to the plot. Here we flesh out this concept, demonstrate that it can be reliably annotated (0.78 Cohen{'}s κ), and provide annotations of 170 narrative texts, drawn from 3 different corpora, containing 1,347 character co-reference chains and 21,999 non-character chains that include 3,937 animate chains. Furthermore, we have shown that a supervised classifier using a simple set of easily computable features can effectively identify these characters (overall F1 of 0.90). A detailed error analysis shows that character identification is first and foremost affected by co-reference quality, and further, that the shorter a chain is the harder it is to effectively identify as a character. We release our code and data for the benefit of other researchers