Search Results for author: Ke Tran

Found 12 papers, 7 papers with code

The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities

no code implementations30 May 2024 David Stap, Eva Hasler, Bill Byrne, Christof Monz, Ke Tran

In particular, we observe a decline in the ability to perform formality steering, to produce technical translations through few-shot examples, and to perform document-level translation.

Machine Translation Translation

The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation

1 code implementation NAACL 2022 Tobias Domhan, Eva Hasler, Ke Tran, Sony Trenous, Bill Byrne, Felix Hieber

Vocabulary selection, or lexical shortlisting, is a well-known technique to improve latency of Neural Machine Translation models by constraining the set of allowed output words during inference.

Machine Translation Sentence +1

From English To Foreign Languages: Transferring Pre-trained Language Models

1 code implementation18 Feb 2020 Ke Tran

With a single GPU, our approach can obtain a foreign BERT base model within a day and a foreign BERT large within two days.

Dependency Parsing Natural Language Inference

Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations

no code implementations WS 2019 Ke Tran, Arianna Bisazza

We investigate whether off-the-shelf deep bidirectional sentence representations trained on a massively multilingual corpus (multilingual BERT) enable the development of an unsupervised universal dependency parser.

Dependency Parsing Sentence +1

Inducing Grammars with and for Neural Machine Translation

no code implementations ACL 2018 Ke Tran, Yonatan Bisk

To address both of these issues we introduce a model that simultaneously translates while inducing dependency trees.

Decoder Machine Translation +2

The Importance of Being Recurrent for Modeling Hierarchical Structure

1 code implementation EMNLP 2018 Ke Tran, Arianna Bisazza, Christof Monz

Recent work has shown that recurrent neural networks (RNNs) can implicitly capture and exploit hierarchical information when trained to solve common natural language processing tasks such as language modeling (Linzen et al., 2016) and neural machine translation (Shi et al., 2016).

Language Modelling Machine Translation +1

Examining Cooperation in Visual Dialog Models

1 code implementation4 Dec 2017 Mircea Mironenco, Dana Kianfar, Ke Tran, Evangelos Kanoulas, Efstratios Gavves

In this work we propose a blackbox intervention method for visual dialog models, with the aim of assessing the contribution of individual linguistic or visual components.

Visual Dialog

Unsupervised Neural Hidden Markov Models

2 code implementations WS 2016 Ke Tran, Yonatan Bisk, Ashish Vaswani, Daniel Marcu, Kevin Knight

In this work, we present the first results for neuralizing an Unsupervised Hidden Markov Model.


Recurrent Memory Networks for Language Modeling

2 code implementations NAACL 2016 Ke Tran, Arianna Bisazza, Christof Monz

In this paper, we propose Recurrent Memory Network (RMN), a novel RNN architecture, that not only amplifies the power of RNN but also facilitates our understanding of its internal functioning and allows us to discover underlying patterns in data.

Language Modelling Sentence +1

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