Search Results for author:
Found 35 papers, 19 papers with code
Controlling Extra-Textual Attributes about Dialogue Participants: A Case Study of English-to-Polish Neural Machine Translation
We focus on the underresearched problem of utilising external metadata in automatic translation of TV dialogue, proposing a case study where a wide range of approaches for controlling attributes in translation is employed in a multi-attribute scenario.
Factored Neural Machine Translation Architectures
A qualitative analysis of the output on a set of test sentences shows the effectiveness of the FNMT model.
Findings of the First Shared Task on Lifelong Learning Machine Translation
A lifelong learning system can adapt to new data without forgetting previously acquired knowledge.
Findings of the IWSLT 2022 Evaluation Campaign
The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation.
MEXMA: Token-level objectives improve sentence representations
Current pre-trained cross-lingual sentence encoders approaches use sentence-level objectives only.
We Need to Talk About Classification Evaluation Metrics in NLP
In Natural Language Processing (NLP) classification tasks such as topic categorisation and sentiment analysis, model generalizability is generally measured with standard metrics such as Accuracy, F-Measure, or AUC-ROC.
Seamless: Multilingual Expressive and Streaming Speech Translation
In this work, we introduce a family of models that enable end-to-end expressive and multilingual translations in a streaming fashion.
automatic-speech-translation
Multimodal Machine Translation
+1
SeamlessM4T: Massively Multilingual & Multimodal Machine Translation
What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages?
Ranked #1 on
Speech-to-Speech Translation
on CVSS
(using extra training data)
Automatic Speech Recognition
Speech-to-Speech Translation
+4
HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translation
Hallucinations in machine translation are translations that contain information completely unrelated to the input.
Metaphor Detection with Effective Context Denoising
We propose a novel RoBERTa-based model, RoPPT, which introduces a target-oriented parse tree structure in metaphor detection.
FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning
In this paper, we propose FrameBERT, a RoBERTa-based model that can explicitly learn and incorporate FrameNet Embeddings for concept-level metaphor detection.
Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Better
We propose to use a method that evaluates the percentage of the source contribution to a generated translation.
Controlling Formality in Low-Resource NMT with Domain Adaptation and Re-Ranking: SLT-CDT-UoS at IWSLT2022
This paper describes the SLT-CDT-UoS group's submission to the first Special Task on Formality Control for Spoken Language Translation, part of the IWSLT 2022 Evaluation Campaign.
Controlling Extra-Textual Attributes about Dialogue Participants -- A Case Study of English-to-Polish Neural Machine Translation
We focus on the underresearched problem of utilising external metadata in automatic translation of TV dialogue, proposing a case study where a wide range of approaches for controlling attributes in translation is employed in a multi-attribute scenario.
ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks
This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2022: low-resource and dialect speech translation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Speech Resources in the Tamasheq Language
In this paper we present two datasets for Tamasheq, a developing language mainly spoken in Mali and Niger.
Active Learning by Acquiring Contrastive Examples
Common acquisition functions for active learning use either uncertainty or diversity sampling, aiming to select difficult and diverse data points from the pool of unlabeled data, respectively.
On the Importance of Effectively Adapting Pretrained Language Models for Active Learning
Recent Active Learning (AL) approaches in Natural Language Processing (NLP) proposed using off-the-shelf pretrained language models (LMs).
Findings of the 2020 Conference on Machine Translation (WMT20)
In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories.
Simultaneous Machine Translation with Visual Context
Simultaneous machine translation (SiMT) aims to translate a continuous input text stream into another language with the lowest latency and highest quality possible.
Probing the Need for Visual Context in Multimodal Machine Translation
Current work on multimodal machine translation (MMT) has suggested that the visual modality is either unnecessary or only marginally beneficial.
Multimodal Grounding for Sequence-to-Sequence Speech Recognition
Specifically, in our previous work, we propose a multistep visual adaptive training approach which improves the accuracy of an audio-based Automatic Speech Recognition (ASR) system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
How2: A Large-scale Dataset for Multimodal Language Understanding
In this paper, we introduce How2, a multimodal collection of instructional videos with English subtitles and crowdsourced Portuguese translations.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
LIUM-CVC Submissions for WMT18 Multimodal Translation Task
This paper describes the multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT18 Shared Task on Multimodal Translation.
What you can cram into a single vector: Probing sentence embeddings for linguistic properties
Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing.
Neural Machine Translation by Generating Multiple Linguistic Factors
FNMT system is designed to manage larger vocabulary and reduce the training time (for systems with equivalent target language vocabulary size).
Findings of the Second Shared Task on Multimodal Machine Translation and Multilingual Image Description
The multilingual image description task was changed such that at test time, only the image is given.
LIUM Machine Translation Systems for WMT17 News Translation Task
This paper describes LIUM submissions to WMT17 News Translation Task for English-German, English-Turkish, English-Czech and English-Latvian language pairs.
LIUM-CVC Submissions for WMT17 Multimodal Translation Task
This paper describes the monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation.
NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation Systems
nmtpy has been used for LIUM's top-ranked submissions to WMT Multimodal Machine Translation and News Translation tasks in 2016 and 2017.
Factored Neural Machine Translation
In addition, we can produce new words that are not in the vocabulary.
Multimodal Attention for Neural Machine Translation
We show that a dedicated attention for each modality achieves up to 1. 6 points in BLEU and METEOR compared to a textual NMT baseline.
Very Deep Convolutional Networks for Text Classification
The dominant approach for many NLP tasks are recurrent neural networks, in particular LSTMs, and convolutional neural networks.
Ranked #17 on
Text Classification
on AG News
Does Multimodality Help Human and Machine for Translation and Image Captioning?
This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge.
