pwc-archive/papers-with-abstracts huggingface dataset:
Forecasting Firm Material Events from 8-K Reports
Authors: Shuang (Sophie) Zhai, Zhu (Drew) Zhang
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: 8k, Decision Making, Deep Learning, Management

In this paper, we show deep learning models can be used to forecast firm material event sequences based on the contents in the company{'}s 8-K Current Reports. Specifically, we exploit state-of-the-art neural architectures, including sequence-to-sequence (Seq2Seq) architecture and attention mechanisms, in the model. Our 8K-powered deep learning model demonstrates promising performance in forecasting firm future event sequences. The model is poised to benefit various stakeholders, including management and investors, by facilitating risk management and decision making.
Complaint Analysis and Classification for Economic and Food Safety
Authors: Jo{\~a}o Filgueiras, Lu{\'\i}s Barbosa, Gil Rocha, Henrique Lopes Cardoso, Lu{\'\i}s Paulo Reis, Jo{\~a}o Pedro Machado, Ana Maria Oliveira
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Classification, General Classification

Governmental institutions are employing artificial intelligence techniques to deal with their specific problems and exploit their huge amounts of both structured and unstructured information. In particular, natural language processing and machine learning techniques are being used to process citizen feedback. In this paper, we report on the use of such techniques for analyzing and classifying complaints, in the context of the Portuguese Economic and Food Safety Authority. Grounded in its operational process, we address three different classification problems: target economic activity, implied infraction severity level, and institutional competence. We show promising results obtained using feature-based approaches and traditional classifiers, with accuracy scores above 70{\%}, and analyze the shortcomings of our current results and avenues for further improvement, taking into account the intended use of our classifiers in helping human officers to cope with thousands of yearly complaints.
Annotation Process for the Dialog Act Classification of a Taglish E-commerce Q\&A Corpus
Authors: Jared Rivera, Jan Caleb Oliver Pensica, Jolene Valenzuela, Alfonso Secuya, Charibeth Cheng
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Classification, Dialog Act Classification, General Classification

With conversational agents or chatbots making up in quantity of replies rather than quality, the need to identify user intent has become a main concern to improve these agents. Dialog act (DA) classification tackles this concern, and while existing studies have already addressed DA classification in general contexts, no training corpora in the context of e-commerce is available to the public. This research addressed the said insufficiency by building a text-based corpus of 7,265 posts from the question and answer section of products on Lazada Philippines. The SWBD-DAMSL tagset for DA classification was modified to 28 tags fitting the categories applicable to e-commerce conversations. The posts were annotated manually by three (3) human annotators and preprocessing techniques decreased the vocabulary size from 6,340 to 1,134. After analysis, the corpus was composed dominantly of single-label posts, with 34{\%} of the corpus having multiple intent tags. The annotated corpus allowed insights toward the structure of posts created with single to multiple intents.
Overview of the 6th Workshop on Asian Translation
Authors: Toshiaki Nakazawa, Nobushige Doi, Shohei Higashiyama, Chenchen Ding, Raj Dabre, Hideya Mino, Isao Goto, Win Pa Pa, Anoop Kunchukuttan, Yusuke Oda, Shantipriya Parida, Ond{\v{r}}ej Bojar, Sadao Kurohashi
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Translation

This paper presents the results of the shared tasks from the 6th workshop on Asian translation (WAT2019) including Ja↔En, Ja↔Zh scientific paper translation subtasks, Ja↔En, Ja↔Ko, Ja↔En patent translation subtasks, Hi↔En, My↔En, Km↔En, Ta↔En mixed domain subtasks and Ru↔Ja news commentary translation task. For the WAT2019, 25 teams participated in the shared tasks. We also received 10 research paper submissions out of which 61 were accepted. About 400 translation results were submitted to the automatic evaluation server, and selected submis- sions were manually evaluated.
Compact and Robust Models for Japanese-English Character-level Machine Translation
Authors: Jinan Dai, Kazunori Yamaguchi
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Machine Translation, Translation

Character-level translation has been proved to be able to achieve preferable translation quality without explicit segmentation, but training a character-level model needs a lot of hardware resources. In this paper, we introduced two character-level translation models which are mid-gated model and multi-attention model for Japanese-English translation. We showed that the mid-gated model achieved the better performance with respect to BLEU scores. We also showed that a relatively narrow beam of width 4 or 5 was sufficient for the mid-gated model. As for unknown words, we showed that the mid-gated model could somehow translate the one containing Katakana by coining out a close word. We also showed that the model managed to produce tolerable results for heavily noised sentences, even though the model was trained with the dataset without noise.
Controlling Japanese Honorifics in English-to-Japanese Neural Machine Translation
Authors: Weston Feely, Eva Hasler, Adri{\`a} de Gispert
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Machine Translation, NMT, Sentence, Translation

In the Japanese language different levels of honorific speech are used to convey respect, deference, humility, formality and social distance. In this paper, we present a method for controlling the level of formality of Japanese output in English-to-Japanese neural machine translation (NMT). By using heuristics to identify honorific verb forms, we classify Japanese sentences as being one of three levels of informal, polite, or formal speech in parallel text. The English source side is marked with a feature that identifies the level of honorific speech present in the Japanese target side. We use this parallel text to train an English-Japanese NMT model capable of producing Japanese translations in different honorific speech styles for the same English input sentence.
English to Hindi Multi-modal Neural Machine Translation and Hindi Image Captioning
Authors: Sahinur Rahman Laskar, Rohit Pratap Singh, Partha Pakray, B, Sivaji yopadhyay
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Hindi Image Captioning, Image Captioning, Machine Translation, NMT, Translation

With the widespread use of Machine Trans-lation (MT) techniques, attempt to minimizecommunication gap among people from di-verse linguistic backgrounds. We have par-ticipated in Workshop on Asian Transla-tion 2019 (WAT2019) multi-modal translationtask. There are three types of submissiontrack namely, multi-modal translation, Hindi-only image captioning and text-only transla-tion for English to Hindi translation. The mainchallenge is to provide a precise MT output.The multi-modal concept incorporates textualand visual features in the translation task. Inthis work, multi-modal translation track re-lies on pre-trained convolutional neural net-works (CNN) with Visual Geometry Grouphaving 19 layered (VGG19) to extract imagefeatures and attention-based Neural MachineTranslation (NMT) system for translation.The merge-model of recurrent neural network(RNN) and CNN is used for the Hindi-onlyimage captioning. The text-only translationtrack is based on the transformer model of theNMT system. The official results evaluated atWAT2019 translation task, which shows thatour multi-modal NMT system achieved Bilin-gual Evaluation Understudy (BLEU) score20.37, Rank-based Intuitive Bilingual Eval-uation Score (RIBES) 0.642838, Adequacy-Fluency Metrics (AMFM) score 0.668260 forchallenge test data and BLEU score 40.55,RIBES 0.760080, AMFM score 0.770860 forevaluation test data in English to Hindi multi-modal translation respectively.
Supervised and Unsupervised Machine Translation for Myanmar-English and Khmer-English
Authors: Benjamin Marie, Hour Kaing, Aye Myat Mon, Chenchen Ding, Atsushi Fujita, Masao Utiyama, Eiichiro Sumita
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Machine Translation, NMT, Translation, Unsupervised Machine Translation

This paper presents the NICT{'}s supervised and unsupervised machine translation systems for the WAT2019 Myanmar-English and Khmer-English translation tasks. For all the translation directions, we built state-of-the-art supervised neural (NMT) and statistical (SMT) machine translation systems, using monolingual data cleaned and normalized. Our combination of NMT and SMT performed among the best systems for the four translation directions. We also investigated the feasibility of unsupervised machine translation for low-resource and distant language pairs and confirmed observations of previous work showing that unsupervised MT is still largely unable to deal with them.
NICT's participation to WAT 2019: Multilingualism and Multi-step Fine-Tuning for Low Resource NMT
Authors: Raj Dabre, Eiichiro Sumita
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Domain Adaptation, Low Resource NMT, NMT, Translation

In this paper we describe our submissions to WAT 2019 for the following tasks: English{--}Tamil translation and Russian{--}Japanese translation. Our team,{``}NICT-5{''}, focused on multilingual domain adaptation and back-translation for Russian{--}Japanese translation and on simple fine-tuning for English{--}Tamil translation . We noted that multi-stage fine tuning is essential in leveraging the power of multilingualism for an extremely low-resource language like Russian{--}Japanese. Furthermore, we can improve the performance of such a low-resource language pair by exploiting a small but in-domain monolingual corpus via back-translation. We managed to obtain second rank in both tasks for all translation directions.
KNU-HYUNDAI's NMT system for Scientific Paper and Patent Tasks onWAT 2019
Authors: Cheoneum Park, Young-Jun Jung, Kihoon Kim, Geonyeong Kim, Jae-Won Jeon, Seongmin Lee, Jun-Seok Kim, Chang-Ki Lee
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Data Augmentation, Machine Translation, NMT, Reranking, Translation

In this paper, we describe the neural machine translation (NMT) system submitted by the Kangwon National University and HYUNDAI (KNU-HYUNDAI) team to the translation tasks of the 6th workshop on Asian Translation (WAT 2019). We participated in all tasks of ASPEC and JPC2, which included those of Chinese-Japanese, English-Japanese, and Korean-{\textgreater}Japanese. We submitted our transformer-based NMT system with built using the following methods: a) relative positioning method for pairwise relationships between the input elements, b) back-translation and multi-source translation for data augmentation, c) right-to-left (r2l)-reranking model robust against error propagation in autoregressive architectures such as decoders, and d) checkpoint ensemble models, which selected the top three models with the best validation bilingual evaluation understudy (BLEU) . We have reported the translation results on the two aforementioned tasks. We performed well in both the tasks and were ranked first in terms of the BLEU scores in all the JPC2 subtasks we participated in.
English-Myanmar Supervised and Unsupervised NMT: NICT's Machine Translation Systems at WAT-2019
Authors: Rui Wang, Haipeng Sun, Kehai Chen, Chenchen Ding, Masao Utiyama, Eiichiro Sumita
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Language Modeling, Language Modelling, Machine Translation, NMT, Translation

This paper presents the NICT{'}s participation (team ID: NICT) in the 6th Workshop on Asian Translation (WAT-2019) shared translation task, specifically Myanmar (Burmese) - English task in both translation directions. We built neural machine translation (NMT) systems for these tasks. Our NMT systems were trained with language model pretraining. Back-translation technology is adopted to NMT. Our NMT systems rank the third in English-to-Myanmar and the second in Myanmar-to-English according to BLEU score.
UCSMNLP: Statistical Machine Translation for WAT 2019
Authors: Aye Thida, Nway Nway Han, Sheinn Thawtar Oo, Khin Thet Htar
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Machine Translation, NER, Reranking, Translation

This paper represents UCSMNLP{'}s submission to the WAT 2019 Translation Tasks focusing on the Myanmar-English translation. Phrase based statistical machine translation (PBSMT) system is built by using other resources: Name Entity Recognition (NER) corpus and bilingual dictionary which is created by Google Translate (GT). This system is also adopted with listwise reranking process in order to improve the quality of translation and tuning is done by changing initial distortion weight. The experimental results show that PBSMT using other resources with initial distortion weight (0.4) and listwise reranking function outperforms the baseline system.
NTT Neural Machine Translation Systems at WAT 2019
Authors: Makoto Morishita, Jun Suzuki, Masaaki Nagata
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Machine Translation, Translation

In this paper, we describe our systems that were submitted to the translation shared tasks at WAT 2019. This year, we participated in two distinct types of subtasks, a scientific paper subtask and a timely disclosure subtask, where we only considered English-to-Japanese and Japanese-to-English translation directions. We submitted two systems (En-Ja and Ja-En) for the scientific paper subtask and two systems (Ja-En, texts, items) for the timely disclosure subtask. Three of our four systems obtained the best human evaluation performances. We also confirmed that our new additional web-crawled parallel corpus improves the performance in unconstrained settings.
Neural Machine Translation System using a Content-equivalently Translated Parallel Corpus for the Newswire Translation Tasks at WAT 2019
Authors: Hideya Mino, Hitoshi Ito, Isao Goto, Ichiro Yamada, Hideki Tanaka, Takenobu Tokunaga
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Machine Translation, Sentence, Translation

This paper describes NHK and NHK Engineering System (NHK-ES){'}s submission to the newswire translation tasks of WAT 2019 in both directions of Japanese→English and English→Japanese. In addition to the JIJI Corpus that was officially provided by the task organizer, we developed a corpus of 0.22M sentence pairs by manually, translating Japanese news sentences into English content- equivalently. The content-equivalent corpus was effective for improving translation quality, and our systems achieved the best human evaluation scores in the newswire translation tasks at WAT 2019.
Combining Translation Memory with Neural Machine Translation
Authors: Akiko Eriguchi, Spencer Rarrick, Hitokazu Matsushita
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Machine Translation, NMT, Sentence, Translation

In this paper, we report our submission systems (geoduck) to the Timely Disclosure task on the 6th Workshop on Asian Translation (WAT) (Nakazawa et al., 2019). Our system employs a combined approach of translation memory and Neural Machine Translation (NMT) models, where we can select final translation outputs from either a translation memory or an NMT system, when the similarity score of a test source sentence exceeds the predefined threshold. We observed that this combination approach significantly improves the translation performance on the Timely Disclosure corpus, as compared to a standalone NMT system. We also conducted source-based direct assessment on the final output, and we discuss the comparison between human references and each system{'}s output.
CVIT's submissions to WAT-2019
Authors: Jerin Philip, Shashank Siripragada, Upendra Kumar, Vinay Namboodiri, C. V. Jawahar
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Machine Translation, Translation

This paper describes the Neural Machine Translation systems used by IIIT Hyderabad (CVIT-MT) for the translation tasks part of WAT-2019. We participated in tasks pertaining to Indian languages and submitted results for English-Hindi, Hindi-English, English-Tamil and Tamil-English language pairs. We employ Transformer architecture experimenting with multilingual models and methods for low-resource languages.
LTRC-MT Simple \& Effective Hindi-English Neural Machine Translation Systems at WAT 2019
Authors: Vikrant Goyal, Dipti Misra Sharma
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Machine Translation, NMT, Translation

This paper describes the Neural Machine Translation systems of IIIT-Hyderabad (LTRC-MT) for WAT 2019 Hindi-English shared task. We experimented with both Recurrent Neural Networks {\&} Transformer architectures. We also show the results of our experiments of training NMT models using additional data via backtranslation.
Long Warm-up and Self-Training: Training Strategies of NICT-2 NMT System at WAT-2019
Authors: Kenji Imamura, Eiichiro Sumita
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Machine Translation, NMT, Translation

This paper describes the NICT-2 neural machine translation system at the 6th Workshop on Asian Translation. This system employs the standard Transformer model but features the following two characteristics. One is the long warm-up strategy, which performs a longer warm-up of the learning rate at the start of the training than conventional approaches. Another is that the system introduces self-training approaches based on multiple back-translations generated by sampling. We participated in three tasks{---}ASPEC.en-ja, ASPEC.ja-en, and TDDC.ja-en{---}using this system.
Supervised neural machine translation based on data augmentation and improved training \& inference process
Authors: Yixuan Tong, Liang Liang, Boyan Liu, Shanshan Jiang, Bin Dong
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Data Augmentation, Machine Translation, Position, Translation

This is the second time for SRCB to participate in WAT. This paper describes the neural machine translation systems for the shared translation tasks of WAT 2019. We participated in ASPEC tasks and submitted results on English-Japanese, Japanese-English, Chinese-Japanese, and Japanese-Chinese four language pairs. We employed the Transformer model as the baseline and experimented relative position representation, data augmentation, deep layer model, ensemble. Experiments show that all these methods can yield substantial improvements.
Sarah's Participation in WAT 2019
Authors: Raymond Hendy Susanto, Ohnmar Htun, Liling Tan
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: NMT

This paper describes our MT systems{'} participation in the of WAT 2019. We participated in the (i) Patent, (ii) Timely Disclosure, (iii) Newswire and (iv) Mixed-domain tasks. Our main focus is to explore how similar Transformer models perform on various tasks. We observed that for tasks with smaller datasets, our best model setup are shallower models with lesser number of attention heads. We investigated practical issues in NMT that often appear in production settings, such as coping with multilinguality and simplifying pre- and post-processing pipeline in deployment.
Our Neural Machine Translation Systems for WAT 2019
Authors: Wei Yang, Jun Ogata
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Machine Translation, NMT, Position, Segmentation, Translation

In this paper, we describe our Neural Machine Translation (NMT) systems for the WAT 2019 translation tasks we focus on. This year we participate in scientific paper tasks and focus on the language pair between English and Japanese. We use Transformer model through our work in this paper to explore and experience the powerful of the Transformer architecture relying on self-attention mechanism. We use different NMT toolkit/library as the implementation of training the Transformer model. For word segmentation, we use different subword segmentation strategies while using different toolkit/library. We not only give the translation accuracy obtained based on absolute position encodings that introduced in the Transformer model, but also report the the improvements in translation accuracy while replacing absolute position encodings with relative position representations. We also ensemble several independent trained Transformer models to further improve the translation accuracy.
Japanese-Russian TMU Neural Machine Translation System using Multilingual Model for WAT 2019
Authors: Aizhan Imankulova, Masahiro Kaneko, Mamoru Komachi
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Machine Translation, NMT, Translation

We introduce our system that is submitted to the News Commentary task (Japanese{\textless}-{\textgreater}Russian) of the 6th Workshop on Asian Translation. The goal of this shared task is to study extremely low resource situations for distant language pairs. It is known that using parallel corpora of different language pair as training data is effective for multilingual neural machine translation model in extremely low resource scenarios. Therefore, to improve the translation quality of Japanese{\textless}-{\textgreater}Russian language pair, our method leverages other in-domain Japanese-English and English-Russian parallel corpora as additional training data for our multilingual NMT model.
NLPRL at WAT2019: Transformer-based Tamil -- English Indic Task Neural Machine Translation System
Authors: Amit Kumar, Anil Kumar Singh
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Machine Translation, Translation

This paper describes the Machine Translation system for Tamil-English Indic Task organized at WAT 2019. We use Transformer- based architecture for Neural Machine Translation.
Idiap NMT System for WAT 2019 Multimodal Translation Task
Authors: Shantipriya Parida, Ond{\v{r}}ej Bojar, Petr Motlicek
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: NMT, Translation

This paper describes the Idiap submission to WAT 2019 for the English-Hindi Multi-Modal Translation Task. We have used the state-of-the-art Transformer model and utilized the IITB English-Hindi parallel corpus as an additional data source. Among the different tracks of the multi-modal task, we have participated in the {``}Text-Only{''} track for the evaluation and challenge test sets. Our submission tops in its track among the competitors in terms of both automatic and manual evaluation. Based on automatic scores, our text-only submission also outperforms systems that consider visual information in the {``}multi-modal translation{''} task.
WAT2019: English-Hindi Translation on Hindi Visual Genome Dataset
Authors: Loitongbam Sanayai Meetei, Thoudam Doren Singh, B, Sivaji yopadhyay
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Caption Generation, Translation

A multimodal translation is a task of translating a source language to a target language with the help of a parallel text corpus paired with images that represent the contextual details of the text. In this paper, we carried out an extensive comparison to evaluate the benefits of using a multimodal approach on translating text in English to a low resource language, Hindi as a part of WAT2019 shared task. We carried out the translation of English to Hindi in three separate tasks with both the evaluation and challenge dataset. First, by using only the parallel text corpora, then through an image caption generation approach and, finally with the multimodal approach. Our experiment shows a significant improvement in the result with the multimodal approach than the other approach.