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Found 24 papers, 10 papers with code
The UPC RDF-to-Text System at WebNLG Challenge 2020
This work describes the end-to-end system architecture presented at WebNLG Challenge 2020.
The TALP-UPC Participation in WMT21 News Translation Task: an mBART-based NMT Approach
We fine-tune mBART50 using the filtered data, and additionally, we train a Transformer model on the same data from scratch.
The TALP-UPC System Description for WMT20 News Translation Task: Multilingual Adaptation for Low Resource MT
In this article, we describe the TALP-UPC participation in the WMT20 news translation shared task for Tamil-English.
Enhancing Sequence-to-Sequence Modelling for RDF triples to Natural Text
establishes key guidelines on how, which and when Machine Translation (MT) techniques are worth applying to RDF-to-Text task.
Language Modelling for Speaker Diarization in Telephonic Interviews
In this study we analyze how an appropriate fusion of both kind of features is able to obtain good results in these cases.
Pushing the Limits of Zero-shot End-to-End Speech Translation
The speech encoder seamlessly integrates with the MT model at inference, enabling direct translation from speech to text, across all languages supported by the MT model.
Promoting Generalized Cross-lingual Question Answering in Few-resource Scenarios via Self-knowledge Distillation
Our approach seeks to enhance cross-lingual QA transfer using a high-performing multilingual model trained on a large-scale dataset, complemented by a few thousand aligned QA examples across languages.
Speech Translation with Foundation Models and Optimal Transport: UPC at IWSLT23
Our Speech Translation systems utilize foundation models for speech (wav2vec 2. 0) and text (mBART50).
SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations
End-to-end Speech Translation is hindered by a lack of available data resources.
Efficient Speech Translation with Dynamic Latent Perceivers
Transformers have been the dominant architecture for Speech Translation in recent years, achieving significant improvements in translation quality.
SHAS: Approaching optimal Segmentation for End-to-End Speech Translation
Speech translation datasets provide manual segmentations of the audios, which are not available in real-world scenarios, and existing segmentation methods usually significantly reduce translation quality at inference time.
End-to-End Speech Translation with Pre-trained Models and Adapters: UPC at IWSLT 2021
Our submission also uses a custom segmentation algorithm that employs pre-trained Wav2Vec 2. 0 for identifying periods of untranscribable text and can bring improvements of 2. 5 to 3 BLEU score on the IWSLT 2019 test set, as compared to the result with the given segmentation.
Ranked #2 on
Speech-to-Text Translation
on MuST-C EN->DE
(using extra training data)
Enabling Zero-shot Multilingual Spoken Language Translation with Language-Specific Encoders and Decoders
On the other hand, Multilingual Neural Machine Translation (MultiNMT) approaches rely on higher-quality and more massive data sets.
Training Multilingual Machine Translation by Alternately Freezing Language-Specific Encoders-Decoders
We propose a modular architecture of language-specific encoder-decoders that constitutes a multilingual machine translation system that can be incrementally extended to new languages without the need for retraining the existing system when adding new languages.
Multilingual Machine Translation: Closing the Gap between Shared and Language-specific Encoder-Decoders
State-of-the-art multilingual machine translation relies on a universal encoder-decoder, which requires retraining the entire system to add new languages.
Syntax-driven Iterative Expansion Language Models for Controllable Text Generation
The dominant language modeling paradigm handles text as a sequence of discrete tokens.
Automatic Spanish Translation of the SQuAD Dataset for Multilingual Question Answering
We then used this dataset to train Spanish QA systems by fine-tuning a Multilingual-BERT model.
From Bilingual to Multilingual Neural Machine Translation by Incremental Training
Multilingual Neural Machine Translation approaches are based on the use of task-specific models and the addition of one more language can only be done by retraining the whole system.
Joint Source-Target Self Attention with Locality Constraints
The dominant neural machine translation models are based on the encoder-decoder structure, and many of them rely on an unconstrained receptive field over source and target sequences.
Ranked #10 on
Machine Translation
on WMT2014 English-French
Towards Interlingua Neural Machine Translation
By adding and forcing this interlingual loss, we are able to train multiple encoders and decoders for each language, sharing a common intermediate representation.
(Self-Attentive) Autoencoder-based Universal Language Representation for Machine Translation
Preliminary results on the WMT 2017 Turkish/English task shows that the proposed architecture is capable of learning a universal language representation and simultaneously training both translation directions with state-of-the-art results.
Character-level Intra Attention Network for Natural Language Inference
Natural language inference (NLI) is a central problem in language understanding.
Character-based Neural Machine Translation
Neural Machine Translation (MT) has reached state-of-the-art results.
Conditional distribution variability measures for causality detection
In this paper we derive variability measures for the conditional probability distributions of a pair of random variables, and we study its application in the inference of causal-effect relationships.
