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Found 32 papers, 9 papers with code
Findings of the Third Workshop on Automatic Simultaneous Translation
This paper reports the results of the shared task we hosted on the Third Workshop of Automatic Simultaneous Translation (AutoSimTrans).
Learning Adaptive Segmentation Policy for End-to-End Simultaneous Translation
End-to-end simultaneous speech-to-text translation aims to directly perform translation from streaming source speech to target text with high translation quality and low latency.
Learning Adaptive Segmentation Policy for Simultaneous Translation
The policy learns to segment the source text by considering possible translations produced by the translation model, maintaining consistency between the segmentation and translation.
Non-Autoregressive Chinese ASR Error Correction with Phonological Training
Automatic Speech Recognition (ASR) is an efficient and widely used input method that transcribes speech signals into text.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Findings of the Second Workshop on Automatic Simultaneous Translation
This paper presents the results of the shared task of the 2nd Workshop on Automatic Simultaneous Translation (AutoSimTrans).
Towards Boosting Many-to-Many Multilingual Machine Translation with Large Language Models
The training paradigm for machine translation has gradually shifted, from learning neural machine translation (NMT) models with extensive parallel corpora to instruction finetuning on multilingual large language models (LLMs) with high-quality translation pairs.
An Empirical Study of Consistency Regularization for End-to-End Speech-to-Text Translation
Consistency regularization methods, such as R-Drop (Liang et al., 2021) and CrossConST (Gao et al., 2023), have achieved impressive supervised and zero-shot performance in the neural machine translation (NMT) field.
Learning Multilingual Sentence Representations with Cross-lingual Consistency Regularization
Multilingual sentence representations are the foundation for similarity-based bitext mining, which is crucial for scaling multilingual neural machine translation (NMT) system to more languages.
Improving Zero-shot Multilingual Neural Machine Translation by Leveraging Cross-lingual Consistency Regularization
The experimental analysis also proves that CrossConST could close the sentence representation gap and better align the representation space.
Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation
We introduce Bi-SimCut: a simple but effective training strategy to boost neural machine translation (NMT) performance.
Ranked #1 on
Machine Translation
on WMT2014 German-English
Mixup Decoding for Diverse Machine Translation
To further improve the faithfulness and diversity of the translations, we propose two simple but effective approaches to select diverse sentence pairs in the training corpus and adjust the interpolation weight for each pair correspondingly.
BSTC: A Large-Scale Chinese-English Speech Translation Dataset
This corpus is expected to promote the research of automatic simultaneous translation as well as the development of practical systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Knowledge Distillation based Ensemble Learning for Neural Machine Translation
Under this framework, we introduce word-level ensemble learning and sequence-level ensemble learning for neural machine translation, where sequence-level ensemble learning is capable of aggregating translation models with different decoding strategies.
Simultaneous Translation
Simultaneous translation, which performs translation concurrently with the source speech, is widely useful in many scenarios such as international conferences, negotiations, press releases, legal proceedings, and medicine.
Synchronous Speech Recognition and Speech-to-Text Translation with Interactive Decoding
Speech-to-text translation (ST), which translates source language speech into target language text, has attracted intensive attention in recent years.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Multi-agent Learning for Neural Machine Translation
Conventional Neural Machine Translation (NMT) models benefit from the training with an additional agent, e. g., dual learning, and bidirectional decoding with one agent decoding from left to right and the other decoding in the opposite direction.
Baidu Neural Machine Translation Systems for WMT19
In this paper we introduce the systems Baidu submitted for the WMT19 shared task on Chinese{\textless}-{\textgreater}English news translation.
DuTongChuan: Context-aware Translation Model for Simultaneous Interpreting
In this paper, we present DuTongChuan, a novel context-aware translation model for simultaneous interpreting.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
End-to-End Speech Translation with Knowledge Distillation
End-to-end speech translation (ST), which directly translates from source language speech into target language text, has attracted intensive attentions in recent years.
Modeling Coherence for Discourse Neural Machine Translation
Discourse coherence plays an important role in the translation of one text.
STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework
Simultaneous translation, which translates sentences before they are finished, is useful in many scenarios but is notoriously difficult due to word-order differences.
Robust Neural Machine Translation with Joint Textual and Phonetic Embedding
Neural machine translation (NMT) is notoriously sensitive to noises, but noises are almost inevitable in practice.
Addressing Troublesome Words in Neural Machine Translation
One of the weaknesses of Neural Machine Translation (NMT) is in handling lowfrequency and ambiguous words, which we refer as troublesome words.
Multi-channel Encoder for Neural Machine Translation
This design of encoder yields relatively uniform composition on source sentence, despite the gating mechanism employed in encoding RNN.
Semi-Supervised Learning for Neural Machine Translation
While end-to-end neural machine translation (NMT) has made remarkable progress recently, NMT systems only rely on parallel corpora for parameter estimation.
Agreement-based Joint Training for Bidirectional Attention-based Neural Machine Translation
The attentional mechanism has proven to be effective in improving end-to-end neural machine translation.
Minimum Risk Training for Neural Machine Translation
We propose minimum risk training for end-to-end neural machine translation.
