Articles

AbstractPrevious work mainly focuses on improving cross-lingual transfer for NLU tasks with a multilingual pretrained encoder (MPE), or improving the performance on supervised machine translation with BERT. However, it is under-explored that whether the MPE can help to facilitate the cross-lingual transferability of NMT model. In this paper, we focus …...

AbstractNon-autoregressive neural machine translation (NART) models suffer from the multi-modality problem which causes translation inconsistency such as token repetition. Most recent approaches have attempted to solve this problem by implicitly modeling dependencies between outputs. In this paper, we introduce AligNART, which leverages full alignment information to explicitly reduce the modality …...

AbstractThis paper proposes a novel architecture, Cross Attention Augmented Transducer (CAAT), for simultaneous translation. The framework aims to jointly optimize the policy and translation models. To effectively consider all possible READ-WRITE simultaneous translation action paths, we adapt the online automatic speech recognition (ASR) model, RNN-T, but remove the strong monotonic …...

In political news media, propaganda techniques are often employed to express one’s political view, or to influence the audience’s stance. Chinese corpora with the  國立清華大學臺灣語言研究與教學研究所；國立中正大學通識教育中心 Institute of Taiwan Languages and Language Teaching, National Tsing Hua University; Center for General Education, National Chung Cheng University E-mail: simon.xian@gmail.com  國立臺東大學通識教育中心（通訊作者） Address …...

The study aims to investigate the use of conjunctive adverbials (CA, hereafter) performing various textual relations in the English writing by Chinese speakers across genres and over time. To begin with, a corpus of one million word was compiled and the corpus interface was constructed. Later, 45 pieces of writing …...

In order to study the effectiveness of the current deep learning-based speech recognition models in the speech recognition tasks of Taiwanese Southern Min Recommended Characters and Taiwan Minnanyu Luomazi Pinyin, this study uses the corpora provided by the 2020 Formosa Speech Recognition Challenge 2020 (FSR-2020) to evalutae some neural-based ASR …...

This paper describes the NTNU ASR system participating in the Formosa Speech Recognition Challenge 2020 (FSR-2020) supported by the Formosa Speech in the Wild project (FSW). FSR-2020 aims at fostering the development of Taiwanese speech recognition. Apart from the issues on tonal and dialectical variations of the Taiwanese language, speech …...

This paper describes the NTNU ASR system participating in the Formosa Speech Recognition Challenge 2020 (FSR-2020) supported by the Formosa Speech in the Wild project (FSW). FSR-2020 aims at fostering the development of Taiwanese speech recognition. Apart from the issues on tonal and dialectical variations of the Taiwanese language, speech …...

In this paper, we describe the system team NSYSU-MITLab implemented for Formosa Speech Recognition Challenge 2020. We use the Transformer architecture  國立中山大學資訊工程學系 Department of Computer Science and Information Engineering National Sun Yat-sen University E-mail: m083040013@g-mail.nsysu.edu.tw; cpchen@mail.cse.nsysu.edu.tw 18 林洪邦與陳嘉平 composed of Multi-head Attention to construct an end-to-end speech recognition system …...

AbstractAlthough the existing Named Entity Recognition (NER) models have achieved promising performance, they suffer from certain drawbacks. The sequence labeling-based NER models do not perform well in recognizing long entities as they focus only on word-level information, while the segment-based NER models which focus on processing segment instead of single …...

AbstractWe present a data-driven, end-to-end approach to transaction-based dialog systems that performs at near-human levels in terms of verbal response quality and factual grounding accuracy. We show that two essential components of the system produce these results: a sufficiently large and diverse, in-domain labeled dataset, and a neural network-based, pre-trained …...

AbstractMaintaining a consistent persona is essential for dialogue agents. Although tremendous advancements have been brought, the limited-scale of annotated personalized dialogue datasets is still a barrier towards training robust and consistent persona-based dialogue models. This work shows how this challenge can be addressed by disentangling persona-based dialogue generation into two …...

AbstractMulti-intent SLU can handle multiple intents in an utterance, which has attracted increasing attention. However, the state-of-the-art joint models heavily rely on autoregressive approaches, resulting in two issues: slow inference speed and information leakage. In this paper, we explore a non-autoregressive model for joint multiple intent detection and slot filling, …...

AbstractIn this paper, we study the task of graph-based constituent parsing in the setting that binarization is not conducted as a pre-processing step, where a constituent tree may consist of nodes with more than two children. Previous graph-based methods on this setting typically generate hidden nodes with the dummy label …...

AbstractEncoder pre-training is promising in end-to-end Speech Translation (ST), given the fact that speech-to-translation data is scarce. But ST encoders are not simple instances of Automatic Speech Recognition (ASR) or Machine Translation (MT) encoders. For example, we find that ASR encoders lack the global context representation, which is necessary for …...

AbstractMultimodal fusion has been proved to improve emotion recognition performance in previous works. However, in real-world applications, we often encounter the problem of missing modality, and which modalities will be missing is uncertain. It makes the fixed multimodal fusion fail in such cases. In this work, we propose a unified …...

AbstractBoth performance and efficiency are crucial factors for sequence labeling tasks in many real-world scenarios. Although the pre-trained models (PTMs) have significantly improved the performance of various sequence labeling tasks, their computational cost is expensive. To alleviate this problem, we extend the recent successful early-exit mechanism to accelerate the inference …...

AbstractThe choice of token vocabulary affects the performance of machine translation. This paper aims to figure out what is a good vocabulary and whether we can find the optimal vocabulary without trial training. To answer these questions, we first provide an alternative understanding of vocabulary from the perspective of information …...

AbstractSigned languages are the primary means of communication for many deaf and hard of hearing individuals. Since signed languages exhibit all the fundamental linguistic properties of natural language, we believe that tools and theories of Natural Language Processing (NLP) are crucial towards its modeling. However, existing research in Sign Language …...

AbstractRecent investigations into the inner-workings of state-of-the-art large-scale pre-trained Transformer-based Natural Language Understanding (NLU) models indicate that they appear to understand human-like syntax, at least to some extent. We provide novel evidence that complicates this claim: we find that state-of-the-art Natural Language Inference (NLI) models assign the same labels to …...