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Found 17 papers, 4 papers with code
Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documents
Using simple concatenation-based DocNMT, we explore the effect of 3 factors on the transfer: the number of teacher languages with document level data, the balance between document and sentence level data at training, and the data condition of parallel documents (genuine vs. backtranslated).
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.
Sentence Boundary Augmentation For Neural Machine Translation Robustness
Specifically, in the context of long-form speech translation systems, where the input transcripts come from Automatic Speech Recognition (ASR), the NMT models have to handle errors including phoneme substitutions, grammatical structure, and sentence boundaries, all of which pose challenges to NMT robustness.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Language-agnostic BERT Sentence Embedding
While BERT is an effective method for learning monolingual sentence embeddings for semantic similarity and embedding based transfer learning (Reimers and Gurevych, 2019), BERT based cross-lingual sentence embeddings have yet to be explored.
Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation
Over the last few years two promising research directions in low-resource neural machine translation (NMT) have emerged.
Re-translation versus Streaming for Simultaneous Translation
There has been great progress in improving streaming machine translation, a simultaneous paradigm where the system appends to a growing hypothesis as more source content becomes available.
Controlling Computation versus Quality for Neural Sequence Models
Further, methods that adapt the amount of computation to the example focus on finding a fixed inference-time computational graph per example, ignoring any external computational budgets or varying inference time limitations.
Re-Translation Strategies For Long Form, Simultaneous, Spoken Language Translation
As this scenario allows for revisions to our incremental translations, we adopt a re-translation approach to simultaneous translation, where the source is repeatedly translated from scratch as it grows.
Simple, Scalable Adaptation for Neural Machine Translation
We evaluate our approach on two tasks: (i) Domain Adaptation and (ii) Massively Multilingual NMT.
Investigating Multilingual NMT Representations at Scale
Multilingual Neural Machine Translation (NMT) models have yielded large empirical success in transfer learning settings.
Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation
The recently proposed massively multilingual neural machine translation (NMT) system has been shown to be capable of translating over 100 languages to and from English within a single model.
Small and Practical BERT Models for Sequence Labeling
We propose a practical scheme to train a single multilingual sequence labeling model that yields state of the art results and is small and fast enough to run on a single CPU.
Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges
We introduce our efforts towards building a universal neural machine translation (NMT) system capable of translating between any language pair.
Monotonic Infinite Lookback Attention for Simultaneous Machine Translation
Simultaneous machine translation begins to translate each source sentence before the source speaker is finished speaking, with applications to live and streaming scenarios.
The Missing Ingredient in Zero-Shot Neural Machine Translation
Multilingual Neural Machine Translation (NMT) models are capable of translating between multiple source and target languages.
Neural Language Correction with Character-Based Attention
Motivated by these issues, we present a neural network-based approach to language correction.
