pwc-archive/papers-with-abstracts huggingface dataset:
Meta Ensemble for Japanese-Chinese Neural Machine Translation: Kyoto-U+ECNU Participation to WAT 2020
Authors: Zhuoyuan Mao, Yibin Shen, Chenhui Chu, Sadao Kurohashi, Cheqing Jin
Date: 2020-12-01 00:00:00
Proceeding: AACL (WAT) 2020 12
Tasks: Denoising, Machine Translation, NMT, Translation

This paper describes the Japanese-Chinese Neural Machine Translation (NMT) system submitted by the joint team of Kyoto University and East China Normal University (Kyoto-U+ECNU) to WAT 2020 (Nakazawa et al.,2020). We participate in APSEC Japanese-Chinese translation task. We revisit several techniques for NMT including various architectures, different data selection and augmentation methods, denoising pre-training, and also some specific tricks for Japanese-Chinese translation. We eventually perform a meta ensemble to combine all of the models into a single model. BLEU results of this meta ensembled model rank the first both on 2 directions of ASPEC Japanese-Chinese translation.
Neural Machine Translation Using Extracted Context Based on Deep Analysis for the Japanese-English Newswire Task at WAT 2020
Authors: Isao Goto, Hideya Mino, Hitoshi Ito, Kazutaka Kinugawa, Ichiro Yamada, Hideki Tanaka
Date: 2020-12-01 00:00:00
Proceeding: AACL (WAT) 2020 12
Tasks: Machine Translation, Translation

This paper describes the system of the NHK-NES team for the WAT 2020 Japanese–English newswire task. There are two main problems in Japanese-English news translation: translation of dropped subjects and compatibility between equivalent translations and English news-style outputs. We address these problems by extracting subjects from the context based on predicate-argument structures and using them as additional inputs, and constructing parallel Japanese-English news sentences equivalently translated from English news sentences. The evaluation results confirm the effectiveness of our context-utilization method.
TMU Japanese-English Multimodal Machine Translation System for WAT 2020
Authors: Hiroto Tamura, Tosho Hirasawa, Masahiro Kaneko, Mamoru Komachi
Date: 2020-12-01 00:00:00
Proceeding: AACL (WAT) 2020 12
Tasks: Data Augmentation, Decoder, Machine Translation, Multimodal Machine Translation, Translation

We introduce our TMU system submitted to the Japanese<->English Multimodal Task (constrained) for WAT 2020 (Nakazawa et al., 2020). This task aims to improve translation performance with the help of another modality (images) associated with the input sentences. In a multimodal translation task, the dataset is, by its nature, a low-resource one. Our method used herein augments the data by generating noisy translations and adding noise to existing training images. Subsequently, we pretrain a translation model on the augmented noisy data, and then fine-tune it on the clean data. We also examine the probabilistic dropping of either the textual or visual context vector in the decoder. This aims to regularize the network to make use of both features while training. The experimental results indicate that translation performance can be improved using our method of textual data augmentation with noising on the target side and probabilistic dropping of either context vector.
HW-TSC’s Participation in the WAT 2020 Indic Languages Multilingual Task
Authors: Zhengzhe Yu, Zhanglin Wu, Xiaoyu Chen, Daimeng Wei, Hengchao Shang, Jiaxin Guo, Zongyao Li, Minghan Wang, Liangyou Li, Lizhi Lei, Hao Yang, Ying Qin
Date: 2020-12-01 00:00:00
Proceeding: AACL (WAT) 2020 12
Tasks: Translation

This paper describes our work in the WAT 2020 Indic Multilingual Translation Task. We participated in all 7 language pairs (En<->Bn/Hi/Gu/Ml/Mr/Ta/Te) in both directions under the constrained condition—using only the officially provided data. Using transformer as a baseline, our Multi->En and En->Multi translation systems achieve the best performances. Detailed data filtering and data domain selection are the keys to performance enhancement in our experiment, with an average improvement of 2.6 BLEU scores for each language pair in the En->Multi system and an average improvement of 4.6 BLEU scores regarding the Multi->En. In addition, we employed language independent adapter to further improve the system performances. Our submission obtains competitive results in the final evaluation.
NICT‘s Submission To WAT 2020: How Effective Are Simple Many-To-Many Neural Machine Translation Models?
Authors: Raj Dabre, Abhisek Chakrabarty
Date: 2020-12-01 00:00:00
Proceeding: AACL (WAT) 2020 12
Tasks: Machine Translation, NMT, Translation

In this paper we describe our team‘s (NICT-5) Neural Machine Translation (NMT) models whose translations were submitted to shared tasks of the 7th Workshop on Asian Translation. We participated in the Indic language multilingual sub-task as well as the NICT-SAP multilingual multi-domain sub-task. We focused on naive many-to-many NMT models which gave reasonable translation quality despite their simplicity. Our observations are twofold: (a.) Many-to-many models suffer from a lack of consistency where the translation quality for some language pairs is very good but for some others it is terrible when compared against one-to-many and many-to-one baselines. (b.) Oversampling smaller corpora does not necessarily give the best translation quality for the language pair associated with that pair.
ODIANLP’s Participation in WAT2020
Authors: Shantipriya Parida, Petr Motlicek, Amulya Ratna Dash, Satya Ranjan Dash, Debasish Kumar Mallick, Satya Prakash Biswal, Priyanka Pattnaik, Biranchi Narayan Nayak, Ondřej Bojar
Date: 2020-12-01 00:00:00
Proceeding: AACL (WAT) 2020 12
Tasks: Hindi Image Captioning, Image Captioning, Translation

This paper describes the ODIANLP submission to WAT 2020. We have participated in the English-Hindi Multimodal task and Indic task. We have used the state-of-the-art Transformer model for the translation task and InceptionResNetV2 for the Hindi Image Captioning task. Our submission tops in English->Hindi Multimodal task in its track and Odia<->English translation tasks. Also, our submissions performed well in the Indic Multilingual tasks.
Multimodal Neural Machine Translation for English to Hindi
Authors: Sahinur Rahman Laskar, Abdullah Faiz Ur Rahman Khilji, Partha Pakray, Sivaji Bandyopadhyay
Date: 2020-12-01 00:00:00
Proceeding: AACL (WAT) 2020 12
Tasks: Machine Translation, NMT, Translation

Machine translation (MT) focuses on the automatic translation of text from one natural language to another natural language. Neural machine translation (NMT) achieves state-of-the-art results in the task of machine translation because of utilizing advanced deep learning techniques and handles issues like long-term dependency, and context-analysis. Nevertheless, NMT still suffers low translation quality for low resource languages. To encounter this challenge, the multi-modal concept comes in. The multi-modal concept combines textual and visual features to improve the translation quality of low resource languages. Moreover, the utilization of monolingual data in the pre-training step can improve the performance of the system for low resource language translations. Workshop on Asian Translation 2020 (WAT2020) organized a translation task for multimodal translation in English to Hindi. We have participated in the same in two-track submission, namely text-only and multi-modal translation with team name CNLP-NITS. The evaluated results are declared at the WAT2020 translation task, which reports that our multi-modal NMT system attained higher scores than our text-only NMT on both challenge and evaluation test set. For the challenge test data, our multi-modal neural machine translation system achieves Bilingual Evaluation Understudy (BLEU) score of 33.57, Rank-based Intuitive Bilingual Evaluation Score (RIBES) 0.754141, Adequacy-Fluency Metrics (AMFM) score 0.787320 and for evaluation test data, BLEU, RIBES, and, AMFM score of 40.51, 0.803208, and 0.820980 for English to Hindi translation respectively.
The ADAPT Centre’s Participation in WAT 2020 English-to-Odia Translation Task
Authors: Prashanth Nayak, Rejwanul Haque, Andy Way
Date: 2020-12-01 00:00:00
Proceeding: AACL (WAT) 2020 12
Tasks: Machine Translation, Translation

This paper describes the ADAPT Centre sub-missions to WAT 2020 for the English-to-Odia translation task. We present the approaches that we followed to try to build competitive machine translation (MT) systems for English-to-Odia. Our approaches include monolingual data selection for creating synthetic data and identifying optimal sets of hyperparameters for the Transformer in a low-resource scenario. Our best MT system produces 4.96BLEU points on the evaluation test set in the English-to-Odia translation task.
NLPRL Odia-English: Indic Language Neural Machine Translation System
Authors: Rupjyoti Baruah, Rajesh Kumar Mundotiya
Date: 2020-12-01 00:00:00
Proceeding: AACL (WAT) 2020 12
Tasks: Machine Translation, NMT, Translation

In this manuscript, we (team name is NLPRL) describe systems description that was submitted to the translation shared tasks at WAT 2020. We describe our model as transformer based NMT by using byte-level based BPE (BBPE). We used the OdiEnCorp 2.0 parallel corpus provided by the shared task organizer where the training, validation, and test data contain 69370, 13544, and 14344 lines of parallel sentences, respectively. The evaluation results show the BLEU score of English-to-Oria below the Organizer (1.34) and Oria-to-English direction shows above the Organizer (11.33).
WT: Wipro AI Submissions to the WAT 2020
Authors: Santanu Pal
Date: 2020-12-01 00:00:00
Proceeding: AACL (WAT) 2020 12
Tasks: Machine Translation, NMT, Translation

In this paper we present an English–Hindi and Hindi–English neural machine translation (NMT) system, submitted to the Translation shared Task organized at WAT 2020. We trained a multilingual NMT system based on transformer architecture. In this paper we show: (i) how effective pre-processing helps to improve performance, (ii) how synthetic data through back-translation from available monolingual data can help in overall translation performance, (iii) how language similarity can aid more onto it. Our submissions ranked 1st in both English to Hindi and Hindi to English translation achieving BLEU 20.80 and 29.59 respectively.
Korean-to-Japanese Neural Machine Translation System using Hanja Information
Authors: Hwichan Kim, Tosho Hirasawa, Mamoru Komachi
Date: 2020-12-01 00:00:00
Proceeding: AACL (WAT) 2020 12
Tasks: Machine Translation, NMT, Translation

In this paper, we describe our TMU neural machine translation (NMT) system submitted for the Patent task (Korean→Japanese) of the 7th Workshop on Asian Translation (WAT 2020, Nakazawa et al., 2020). We propose a novel method to train a Korean-to-Japanese translation model. Specifically, we focus on the vocabulary overlap of Korean Hanja words and Japanese Kanji words, and propose strategies to leverage Hanja information. Our experiment shows that Hanja information is effective within a specific domain, leading to an improvement in the BLEU scores by +1.09 points compared to the baseline.
Goku’s Participation in WAT 2020
Authors: Dongzhe Wang, Ohnmar Htun
Date: 2020-12-01 00:00:00
Proceeding: AACL (WAT) 2020 12
Tasks: Decoder, Machine Translation, Sentence, Translation

This paper introduces our neural machine translation systems’ participation in the WAT 2020 (team ID: goku20). We participated in the (i) Patent, (ii) Business Scene Dialogue (BSD) document-level translation, (iii) Mixed-domain tasks. Regardless of simplicity, standard Transformer models have been proven to be very effective in many machine translation systems. Recently, some advanced pre-training generative models have been proposed on the basis of encoder-decoder framework. Our main focus of this work is to explore how robust Transformer models perform in translation from sentence-level to document-level, from resource-rich to low-resource languages. Additionally, we also investigated the improvement that fine-tuning on the top of pre-trained transformer-based models can achieve on various tasks.
The ADAPT Centre’s Neural MT Systems for the WAT 2020 Document-Level Translation Task
Authors: Wandri Jooste, Rejwanul Haque, Andy Way
Date: 2020-12-01 00:00:00
Proceeding: AACL (WAT) 2020 12
Tasks: Data Augmentation, Machine Translation, Translation

In this paper we describe the ADAPT Centre’s submissions to the WAT 2020 document-level Business Scene Dialogue (BSD) Translation task. We only consider translating from Japanese to English for this task and we use the MarianNMT toolkit to train Transformer models. In order to improve the translation quality, we made use of both in-domain and out-of-domain data for training our Machine Translation (MT) systems, as well as various data augmentation techniques for fine-tuning the model parameters. This paper outlines the experiments we ran to train our systems and report the accuracy achieved through these various experiments.
The University of Tokyo’s Submissions to the WAT 2020 Shared Task
Authors: Matīss Rikters, Toshiaki Nakazawa, Ryokan Ri
Date: 2020-12-01 00:00:00
Proceeding: AACL (WAT) 2020 12
Tasks: NMT, Translation

The paper describes the development process of the The University of Tokyo’s NMT systems that were submitted to the WAT 2020 Document-level Business Scene Dialogue Translation sub-task. We describe the data processing workflow, NMT system training architectures, and automatic evaluation results. For the WAT 2020 shared task, we submitted 12 systems (both constrained and unconstrained) for English-Japanese and Japanese-English translation directions. The submitted systems were trained using Transformer models and one was a SMT baseline.
Improving NMT via Filtered Back Translation
Authors: Nikhil Jaiswal, Mayur Patidar, Surabhi Kumari, Manasi Patwardhan, Shirish Karande, Puneet Agarwal, Lovekesh Vig
Date: 2020-12-01 00:00:00
Proceeding: AACL (WAT) 2020 12
Tasks: Document Level Machine Translation, Machine Translation, NMT, Sentence, Translation

Document-Level Machine Translation (MT) has become an active research area among the NLP community in recent years. Unlike sentence-level MT, which translates the sentences independently, document-level MT aims to utilize contextual information while translating a given source sentence. This paper demonstrates our submission (Team ID - DEEPNLP) to the Document-Level Translation task organized by WAT 2020. This task focuses on translating texts from a business dialog corpus while optionally utilizing the context present in the dialog. In our proposed approach, we utilize publicly available parallel corpus from different domains to train an open domain base NMT model. We then use monolingual target data to create filtered pseudo parallel data and employ Back-Translation to fine-tune the base model. This is further followed by fine-tuning on the domain-specific corpus. We also ensemble various models to improvise the translation performance. Our best models achieve a BLEU score of 26.59 and 22.83 in an unconstrained setting and 15.10 and 10.91 in the constrained settings for En->Ja & Ja->En direction, respectively.
An Error-based Investigation of Statistical and Neural Machine Translation Performance on Hindi-to-Tamil and English-to-Tamil
Authors: Akshai Ramesh, Venkatesh Balavadhani parthasa, Rejwanul Haque, Andy Way
Date: 2020-12-01 00:00:00
Proceeding: AACL (WAT) 2020 12
Tasks: Machine Translation, NMT, Translation

Statistical machine translation (SMT) was the state-of-the-art in machine translation (MT) research for more than two decades, but has since been superseded by neural MT (NMT). Despite producing state-of-the-art results in many translation tasks, neural models underperform in resource-poor scenarios. Despite some success, none of the present-day benchmarks that have tried to overcome this problem can be regarded as a universal solution to the problem of translation of many low-resource languages. In this work, we investigate the performance of phrase-based SMT (PB-SMT) and NMT on two rarely-tested low-resource language-pairs, English-to-Tamil and Hindi-to-Tamil, taking a specialised data domain (software localisation) into consideration. This paper demonstrates our findings including the identification of several issues of the current neural approaches to low-resource domain-specific text translation.
History to Myths: Social Network Analysis for Comparison of Stories over Time
Authors: Clément Besnier
Date: 2020-12-01 00:00:00
Proceeding: COLING (LaTeCHCLfL, CLFL, LaTeCH) 2020 12

We discuss on how related stories can be compared by their characters. We investigate character graphs, or social networks, in order to measure evolution of character importance over time. To illustrate this, we chose the Siegfried-Sigurd myth that may come from a Merovingian king named Sigiberthus. The Nibelungenlied, the Völsunga saga and the History of the Franks are the three resources used.
Automatic Topological Field Identification in (Historical) German Texts
Authors: Katrin Ortmann
Date: 2020-12-01 00:00:00
Proceeding: COLING (LaTeCHCLfL, CLFL, LaTeCH) 2020 12
Tasks: Sentence

For the study of certain linguistic phenomena and their development over time, large amounts of textual data must be enriched with relevant annotations. Since the manual creation of such annotations requires a lot of effort, automating the process with NLP methods would be convenient. But the required amounts of training data are usually not available for non-standard or historical language. The present study investigates whether models trained on modern newspaper text can be used to automatically identify topological fields, i.e. syntactic structures, in different modern and historical German texts. The evaluation shows that, in general, it is possible to transfer a parser model to other registers or time periods with overall F1-scores >92%. However, an error analysis makes clear that additional rules and domain-specific training data would be beneficial if sentence structures differ significantly from the training data, e.g. in the case of Early New High German.
A Survey on Approaches to Computational Humor Generation
Authors: Miriam Amin, Manuel Burghardt
Date: 2020-12-01 00:00:00
Proceeding: COLING (LaTeCHCLfL, CLFL, LaTeCH) 2020 12
Tasks: Survey

We provide a comprehensive overview of existing systems for the computational generation of verbal humor in the form of jokes and short humorous texts. Considering linguistic humor theories, we analyze the systematic strengths and drawbacks of the different approaches. In addition, we show how the systems have been evaluated so far and propose two evaluation criteria: humorousness and complexity. From our analysis of the field, we conclude new directions for the advancement of computational humor generation.
Neural Machine Translation of Artwork Titles Using Iconclass Codes
Authors: Nikolay Banar, Walter Daelemans, Mike Kestemont
Date: 2020-12-01 00:00:00
Proceeding: COLING (LaTeCHCLfL, CLFL, LaTeCH) 2020 12
Tasks: Machine Translation, Translation

We investigate the use of Iconclass in the context of neural machine translation for NL<->EN artwork titles. Iconclass is a widely used iconographic classification system used in the cultural heritage domain to describe and retrieve subjects represented in the visual arts. The resource contains keywords and definitions to encode the presence of objects, people, events and ideas depicted in artworks, such as paintings. We propose a simple concatenation approach that improves the quality of automatically generated title translations for artworks, by leveraging textual information extracted from Iconclass. Our results demonstrate that a neural machine translation system is able to exploit this metadata to boost the translation performance of artwork titles. This technology enables interesting applications of machine learning in resource-scarce domains in the cultural sector.
A Two-Step Approach for Automatic OCR Post-Correction
Authors: Robin Schaefer, Clemens Neudecker
Date: 2020-12-01 00:00:00
Proceeding: COLING (LaTeCHCLfL, CLFL, LaTeCH) 2020 12
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), Vocal Bursts Valence Prediction

The quality of Optical Character Recognition (OCR) is a key factor in the digitisation of historical documents. OCR errors are a major obstacle for downstream tasks and have hindered advances in the usage of the digitised documents. In this paper we present a two-step approach to automatic OCR post-correction. The first component is responsible for detecting erroneous sequences in a set of OCRed texts, while the second is designed for correcting OCR errors in them. We show that applying the preceding detection model reduces both the character error rate (CER) compared to a simple one-step correction model and the amount of falsely changed correct characters.
“Shakespeare in the Vectorian Age” – An evaluation of different word embeddings and NLP parameters for the detection of Shakespeare quotes
Authors: Bernhard Liebl, Manuel Burghardt
Date: 2020-12-01 00:00:00
Proceeding: COLING (LaTeCHCLfL, CLFL, LaTeCH) 2020 12
Tasks: Word Embeddings

In this paper we describe an approach for the computer-aided identification of Shakespearean intertextuality in a corpus of contemporary fiction. We present the Vectorian, which is a framework that implements different word embeddings and various NLP parameters. The Vectorian works like a search engine, i.e. a Shakespeare phrase can be entered as a query, the underlying collection of fiction books is then searched for the phrase and the passages that are likely to contain the phrase, either verbatim or as a paraphrase, are presented in a ranked results list. While the Vectorian can be used via a GUI, in which many different parameters can be set and combined manually, in this paper we present an ablation study that automatically evaluates different embedding and NLP parameter combinations against a ground truth. We investigate the behavior of different parameters during the evaluation and discuss how our results may be used for future studies on the detection of Shakespearean intertextuality.
Vital Records: Uncover the past from historical handwritten records
Authors: Herve Dejean, Jean-Luc Meunier
Date: 2020-12-01 00:00:00
Proceeding: COLING (LaTeCHCLfL, CLFL, LaTeCH) 2020 12
Tasks: Handwritten Text Recognition

We present Vital Records, a demonstrator based on deep-learning approaches to handwritten-text recognition, table processing and information extraction, which enables data from century-old documents to be parsed and analysed, making it possible to explore death records in space and time. This demonstrator provides a user interface for browsing and visualising data extracted from 80,000 handwritten pages of tabular data.
Measuring the Effects of Bias in Training Data for Literary Classification
Authors: Sunyam Bagga, Andrew Piper
Date: 2020-12-01 00:00:00
Proceeding: COLING (LaTeCHCLfL, CLFL, LaTeCH) 2020 12

Downstream effects of biased training data have become a major concern of the NLP community. How this may impact the automated curation and annotation of cultural heritage material is currently not well known. In this work, we create an experimental framework to measure the effects of different types of stylistic and social bias within training data for the purposes of literary classification, as one important subclass of cultural material. Because historical collections are often sparsely annotated, much like our knowledge of history is incomplete, researchers often cannot know the underlying distributions of different document types and their various sub-classes. This means that bias is likely to be an intrinsic feature of training data when it comes to cultural heritage material. Our aim in this study is to investigate which classification methods may help mitigate the effects of different types of bias within curated samples of training data. We find that machine learning techniques such as BERT or SVM are robust against reproducing the different kinds of bias within our test data, except in the most extreme cases. We hope that this work will spur further research into the potential effects of bias within training data for other cultural heritage material beyond the study of literature.