pwc-archive/papers-with-abstracts huggingface dataset:
CASIA's System for IWSLT 2020 Open Domain Translation
Authors: Qian Wang, Yuchen Liu, Cong Ma, Yu Lu, Yining Wang, Long Zhou, Yang Zhao, Jiajun Zhang, Cheng-qing Zong
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Knowledge Distillation, Machine Translation, Translation

This paper describes the CASIA{'}s system for the IWSLT 2020 open domain translation task. This year we participate in both Chinese→Japanese and Japanese→Chinese translation tasks. Our system is neural machine translation system based on Transformer model. We augment the training data with knowledge distillation and back translation to improve the translation performance. Domain data classification and weighted domain model ensemble are introduced to generate the final translation result. We compare and analyze the performance on development data with different model settings and different data processing techniques.
Deep Blue Sonics' Submission to IWSLT 2020 Open Domain Translation Task
Authors: Enmin Su, Yi Ren
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Translation

We present in this report our submission to IWSLT 2020 Open Domain Translation Task. We built a data pre-processing pipeline to efficiently handle large noisy web-crawled corpora, which boosts the BLEU score of a widely used transformer model in this translation task. To tackle the open-domain nature of this task, back- translation is applied to further improve the translation performance.
University of Tsukuba's Machine Translation System for IWSLT20 Open Domain Translation Task
Authors: Hongyi Cui, Yizhen Wei, Shohei Iida, Takehito Utsuro, Masaaki Nagata
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Machine Translation, Reranking, Translation

In this paper, we introduce University of Tsukuba{'}s submission to the IWSLT20 Open Domain Translation Task. We participate in both Chinese→Japanese and Japanese→Chinese directions. For both directions, our machine translation systems are based on the Transformer architecture. Several techniques are integrated in order to boost the performance of our models: data filtering, large-scale noised training, model ensemble, reranking and postprocessing. Consequently, our efforts achieve 33.0 BLEU scores for Chinese→Japanese translation and 32.3 BLEU scores for Japanese→Chinese translation.
Xiaomi's Submissions for IWSLT 2020 Open Domain Translation Task
Authors: Yuhui Sun, Mengxue Guo, Xiang Li, Jianwei Cui, Bin Wang
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Domain Adaptation, Knowledge Distillation, Re-Ranking, Translation

This paper describes the Xiaomi{'}s submissions to the IWSLT20 shared open domain translation task for Chinese{\textless}-{\textgreater}Japanese language pair. We explore different model ensembling strategies based on recent Transformer variants. We also further strengthen our systems via some effective techniques, such as data filtering, data selection, tagged back translation, domain adaptation, knowledge distillation, and re-ranking. Our resulting Chinese-{\textgreater}Japanese primary system ranked second in terms of character-level BLEU score among all submissions. Our resulting Japanese-{\textgreater}Chinese primary system also achieved a competitive performance.
ISTIC's Neural Machine Translation System for IWSLT'2020
Authors: jiaze wei, wenbin liu, zhenfeng wu, you pan, yanqing he
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Machine Translation, Translation

This paper introduces technical details of machine translation system of Institute of Scientific and Technical Information of China (ISTIC) for the 17th International Conference on Spoken Language Translation (IWSLT 2020). ISTIC participated in both translation tasks of the Open Domain Translation track: Japanese-to-Chinese MT task and Chinese-to-Japanese MT task. The paper mainly elaborates on the model framework, data preprocessing methods and decoding strategies adopted in our system. In addition, the system performance on the development set are given under different settings.
Octanove Labs' Japanese-Chinese Open Domain Translation System
Authors: Masato Hagiwara
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Machine Translation, NMT, Translation

This paper describes Octanove Labs{'} submission to the IWSLT 2020 open domain translation challenge. In order to build a high-quality Japanese-Chinese neural machine translation (NMT) system, we use a combination of 1) parallel corpus filtering and 2) back-translation. We have shown that, by using heuristic rules and learned classifiers, the size of the parallel data can be reduced by 70{\%} to 90{\%} without much impact on the final MT performance. We have also shown that including the artificially generated parallel data through back-translation further boosts the metric by 17{\%} to 27{\%}, while self-training contributes little. Aside from a small number of parallel sentences annotated for filtering, no external resources have been used to build our system.
NAIST's Machine Translation Systems for IWSLT 2020 Conversational Speech Translation Task
Authors: Ryo Fukuda, Katsuhito Sudoh, Satoshi Nakamura
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Domain Adaptation, Machine Translation, NMT, Style Transfer, Translation

This paper describes NAIST{'}s NMT system submitted to the IWSLT 2020 conversational speech translation task. We focus on the translation disfluent speech transcripts that include ASR errors and non-grammatical utterances. We tried a domain adaptation method by transferring the styles of out-of-domain data (United Nations Parallel Corpus) to be like in-domain data (Fisher transcripts). Our system results showed that the NMT model with domain adaptation outperformed a baseline. In addition, slight improvement by the style transfer was observed.
Generating Fluent Translations from Disfluent Text Without Access to Fluent References: IIT Bombay@IWSLT2020
Authors: Nikhil Saini, Jyotsana Khatri, Preethi Jyothi, Pushpak Bhattacharyya
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Denoising, Machine Translation, Translation

Machine translation systems perform reasonably well when the input is well-formed speech or text. Conversational speech is spontaneous and inherently consists of many disfluencies. Producing fluent translations of disfluent source text would typically require parallel disfluent to fluent training data. However, fluent translations of spontaneous speech are an additional resource that is tedious to obtain. This work describes the submission of IIT Bombay to the Conversational Speech Translation challenge at IWSLT 2020. We specifically tackle the problem of disfluency removal in disfluent-to-fluent text-to-text translation assuming no access to fluent references during training. Common patterns of disfluency are extracted from disfluent references and a noise induction model is used to simulate them starting from a clean monolingual corpus. This synthetically constructed dataset is then considered as a proxy for labeled data during training. We also make use of additional fluent text in the target language to help generate fluent translations. This work uses no fluent references during training and beats a baseline model by a margin of 4.21 and 3.11 BLEU points where the baseline uses disfluent and fluent references, respectively. Index Terms- disfluency removal, machine translation, noise induction, leveraging monolingual data, denoising for disfluency removal.
The HW-TSC Video Speech Translation System at IWSLT 2020
Authors: Minghan Wang, Hao Yang, Yao Deng, Ying Qin, Lizhi Lei, Daimeng Wei, Hengchao Shang, Ning Xie, Xiaochun Li, Jiaxian Guo
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: NMT, Translation

The paper presents details of our system in the IWSLT Video Speech Translation evaluation. The system works in a cascade form, which contains three modules: 1) A proprietary ASR system. 2) A disfluency correction system aims to remove interregnums or other disfluent expressions with a fine-tuned BERT and a series of rule-based algorithms. 3) An NMT System based on the Transformer and trained with massive publicly available corpus.
CUNI Neural ASR with Phoneme-Level Intermediate Step for\textasciitildeNon-Native\textasciitildeSLT at IWSLT 2020
Authors: Peter Pol{\'a}k, Sangeet Sagar, Dominik Mach{\'a}{\v{c}}ek, Ond{\v{r}}ej Bojar
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Translation

In this paper, we present our submission to the Non-Native Speech Translation Task for IWSLT 2020. Our main contribution is a proposed speech recognition pipeline that consists of an acoustic model and a phoneme-to-grapheme model. As an intermediate representation, we utilize phonemes. We demonstrate that the proposed pipeline surpasses commercially used automatic speech recognition (ASR) and submit it into the ASR track. We complement this ASR with off-the-shelf MT systems to take part also in the speech translation track.
Towards Stream Translation: Adaptive Computation Time for Simultaneous Machine Translation
Authors: Felix Schneider, Alex Waibel, er
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, Sentence, speech-recognition, Speech Recognition, Translation

Simultaneous machine translation systems rely on a policy to schedule read and write operations in order to begin translating a source sentence before it is complete. In this paper, we demonstrate the use of Adaptive Computation Time (ACT) as an adaptive, learned policy for simultaneous machine translation using the transformer model and as a more numerically stable alternative to Monotonic Infinite Lookback Attention (MILk). We achieve state-of-the-art results in terms of latency-quality tradeoffs. We also propose a method to use our model on unsegmented input, i.e. without sentence boundaries, simulating the condition of translating output from automatic speech recognition. We present first benchmark results on this task.
How Human is Machine Translationese? Comparing Human and Machine Translations of Text and Speech
Authors: Yuri Bizzoni, Tom S Juzek, Cristina Espa{\~n}a-Bonet, Koel Dutta Chowdhury, Josef van Genabith, Elke Teich
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Machine Translation, Translation

Translationese is a phenomenon present in human translations, simultaneous interpreting, and even machine translations. Some translationese features tend to appear in simultaneous interpreting with higher frequency than in human text translation, but the reasons for this are unclear. This study analyzes translationese patterns in translation, interpreting, and machine translation outputs in order to explore possible reasons. In our analysis we {--} (i) detail two non-invasive ways of detecting translationese and (ii) compare translationese across human and machine translations from text and speech. We find that machine translation shows traces of translationese, but does not reproduce the patterns found in human translation, offering support to the hypothesis that such patterns are due to the model (human vs machine) rather than to the data (written vs spoken).
Findings of the Fourth Workshop on Neural Generation and Translation
Authors: Kenneth Heafield, Hiroaki Hayashi, Yusuke Oda, Ioannis Konstas, Andrew Finch, Graham Neubig, Xi-An Li, Alex Birch, ra
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Machine Translation, NMT, Translation

We describe the finding of the Fourth Workshop on Neural Generation and Translation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2020). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the three shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document-level generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language and 3) STAPLE task: creation of as many possible translations of a given input text. This last shared task was organised by Duolingo.
Compressing Neural Machine Translation Models with 4-bit Precision
Authors: Alham Fikri Aji, Kenneth Heafield
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Machine Translation, NMT, Quantization, Translation

Neural Machine Translation (NMT) is resource-intensive. We design a quantization procedure to compress fit NMT models better for devices with limited hardware capability. We use logarithmic quantization, instead of the more commonly used fixed-point quantization, based on the empirical fact that parameters distribution is not uniform. We find that biases do not take a lot of memory and show that biases can be left uncompressed to improve the overall quality without affecting the compression rate. We also propose to use an error-feedback mechanism during retraining, to preserve the compressed model as a stale gradient. We empirically show that NMT models based on Transformer or RNN architecture can be compressed up to 4-bit precision without any noticeable quality degradation. Models can be compressed up to binary precision, albeit with lower quality. RNN architecture seems to be more robust towards compression, compared to the Transformer.
Training and Inference Methods for High-Coverage Neural Machine Translation
Authors: Michael Yang, Yixin Liu, Rahul Mayuranath
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Diversity, Machine Translation, Translation, Vocal Bursts Intensity Prediction

In this paper, we introduce a system built for the Duolingo Simultaneous Translation And Paraphrase for Language Education (STAPLE) shared task at the 4th Workshop on Neural Generation and Translation (WNGT 2020). We participated in the English-to-Japanese track with a Transformer model pretrained on the JParaCrawl corpus and fine-tuned in two steps on the JESC corpus and then the (smaller) Duolingo training corpus. First, during training, we find it is essential to deliberately expose the model to higher-quality translations more often during training for optimal translation performance. For inference, encouraging a small amount of diversity with Diverse Beam Search to improve translation coverage yielded marginal improvement over regular Beam Search. Finally, using an auxiliary filtering model to filter out unlikely candidates from Beam Search improves performance further. We achieve a weighted F1 score of 27.56{\%} on our own test set, outperforming the STAPLE AWS translations baseline score of 4.31{\%}.
Meeting the 2020 Duolingo Challenge on a Shoestring
Authors: Tadashi Nomoto
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Decoder, Diversity, Sentence

What is given below is a brief description of the two systems, called gFCONV and c-VAE, which we built in a response to the 2020 Duolingo Challenge. Both are neural models that aim at disrupting a sentence representation the encoder generates with an eye on increasing the diversity of sentences that emerge out of the process. Importantly, we decided not to turn to external sources for extra ammunition, curious to know how far we can go while confining ourselves to the data released by Duolingo. gFCONV works by taking over a pre-trained sequence model, and intercepting the output its encoder produces on its way to the decoder. c-VAE is a conditional variational auto-encoder, seeking the diversity by blurring the representation that the encoder derives. Experiments on a corpus constructed out of the public dataset from Duolingo, containing some 4 million pairs of sentences, found that gFCONV is a consistent winner over c-VAE though both suffered heavily from a low recall.
English-to-Japanese Diverse Translation by Combining Forward and Backward Outputs
Authors: Masahiro Kaneko, Aizhan Imankulova, Tosho Hirasawa, Mamoru Komachi
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Diversity, Machine Translation, NMT, Sentence, Translation

We introduce our TMU system that is submitted to The 4th Workshop on Neural Generation and Translation (WNGT2020) to English-to-Japanese (En→Ja) track on Simultaneous Translation And Paraphrase for Language Education (STAPLE) shared task. In most cases machine translation systems generate a single output from the input sentence, however, in order to assist language learners in their journey with better and more diverse feedback, it is helpful to create a machine translation system that is able to produce diverse translations of each input sentence. However, creating such systems would require complex modifications in a model to ensure the diversity of outputs. In this paper, we investigated if it is possible to create such systems in a simple way and whether it can produce desired diverse outputs. In particular, we combined the outputs from forward and backward neural translation models (NMT). Our system achieved third place in En→Ja track, despite adopting only a simple approach.
POSTECH Submission on Duolingo Shared Task
Authors: Junsu Park, Hong-Seok Kwon, Jong-Hyeok Lee
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Transfer Learning, Translation

In this paper, we propose a transfer learning based simultaneous translation model by extending BART. We pre-trained BART with Korean Wikipedia and a Korean news dataset, and fine-tuned with an additional web-crawled parallel corpus and the 2020 Duolingo official training dataset. In our experiments on the 2020 Duolingo test dataset, our submission achieves 0.312 in weighted macro F1 score, and ranks second among the submitted En-Ko systems.
The ADAPT System Description for the STAPLE 2020 English-to-Portuguese Translation Task
Authors: Rejwanul Haque, Yasmin Moslem, Andy Way
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Machine Translation, Sentence, Translation

This paper describes the ADAPT Centre{'}s submission to STAPLE (Simultaneous Translation and Paraphrase for Language Education) 2020, a shared task of the 4th Workshop on Neural Generation and Translation (WNGT), for the English-to-Portuguese translation task. In this shared task, the participants were asked to produce high-coverage sets of plausible translations given English prompts (input source sentences). We present our English-to-Portuguese machine translation (MT) models that were built applying various strategies, e.g. data and sentence selection, monolingual MT for generating alternative translations, and combining multiple n-best translations. Our experiments show that adding the aforementioned techniques to the baseline yields an excellent performance in the English-to-Portuguese translation task.
Expand and Filter: CUNI and LMU Systems for the WNGT 2020 Duolingo Shared Task
Authors: Jind{\v{r}}ich Libovick{\'y}, Zden{\v{e}}k Kasner, Jind{\v{r}}ich Helcl, Ond{\v{r}}ej Du{\v{s}}ek
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Diversity, Translation

We present our submission to the Simultaneous Translation And Paraphrase for Language Education (STAPLE) challenge. We used a standard Transformer model for translation, with a crosslingual classifier predicting correct translations on the output n-best list. To increase the diversity of the outputs, we used additional data to train the translation model, and we trained a paraphrasing model based on the Levenshtein Transformer architecture to generate further synonymous translations. The paraphrasing results were again filtered using our classifier. While the use of additional data and our classifier filter were able to improve results, the paraphrasing model produced too many invalid outputs to further improve the output quality. Our model without the paraphrasing component finished in the middle of the field for the shared task, improving over the best baseline by a margin of 10-22 {\%} weighted F1 absolute.
Exploring Model Consensus to Generate Translation Paraphrases
Authors: Zhenhao Li, Marina Fomicheva, Lucia Specia
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Diversity, Machine Translation, Mixture-of-Experts, model, Translation

This paper describes our submission to the 2020 Duolingo Shared Task on Simultaneous Translation And Paraphrase for Language Education (STAPLE). This task focuses on improving the ability of neural MT systems to generate diverse translations. Our submission explores various methods, including N-best translation, Monte Carlo dropout, Diverse Beam Search, Mixture of Experts, Ensembling, and Lexical Substitution. Our main submission is based on the integration of multiple translations from multiple methods using Consensus Voting. Experiments show that the proposed approach achieves a considerable degree of diversity without introducing noisy translations. Our final submission achieves a 0.5510 weighted F1 score on the blind test set for the English-Portuguese track.
Generating Diverse Translations via Weighted Fine-tuning and Hypotheses Filtering for the Duolingo STAPLE Task
Authors: Sweta Agrawal, Marine Carpuat
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Machine Translation, Translation

This paper describes the University of Maryland{'}s submission to the Duolingo Shared Task on Simultaneous Translation And Paraphrase for Language Education (STAPLE). Unlike the standard machine translation task, STAPLE requires generating a set of outputs for a given input sequence, aiming to cover the space of translations produced by language learners. We adapt neural machine translation models to this requirement by (a) generating n-best translation hypotheses from a model fine-tuned on learner translations, oversampled to reflect the distribution of learner responses, and (b) filtering hypotheses using a feature-rich binary classifier that directly optimizes a close approximation of the official evaluation metric. Combination of systems that use these two strategies achieves F1 scores of 53.9{\%} and 52.5{\%} on Vietnamese and Portuguese, respectively ranking 2nd and 4th on the leaderboard.
The JHU Submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education
Authors: Huda Khayrallah, Jacob Bremerman, Arya D. McCarthy, Kenton Murray, Winston Wu, Matt Post
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Machine Translation, Translation

This paper presents the Johns Hopkins University submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education (STAPLE). We participated in all five language tasks, placing first in each. Our approach involved a language-agnostic pipeline of three components: (1) building strong machine translation systems on general-domain data, (2) fine-tuning on Duolingo-provided data, and (3) generating n-best lists which are then filtered with various score-based techniques. In addi- tion to the language-agnostic pipeline, we attempted a number of linguistically-motivated approaches, with, unfortunately, little success. We also find that improving BLEU performance of the beam-search generated translation does not necessarily improve on the task metric{---}weighted macro F1 of an n-best list.
Efficient and High-Quality Neural Machine Translation with OpenNMT
Authors: Guillaume Klein, Dakun Zhang, Cl{\'e}ment Chouteau, Josep Crego, Jean Senellart
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: CPU, GPU, Machine Translation, Translation, Vocal Bursts Intensity Prediction

This paper describes the OpenNMT submissions to the WNGT 2020 efficiency shared task. We explore training and acceleration of Transformer models with various sizes that are trained in a teacher-student setup. We also present a custom and optimized C++ inference engine that enables fast CPU and GPU decoding with few dependencies. By combining additional optimizations and parallelization techniques, we create small, efficient, and high-quality neural machine translation models.