Search Results for author:
Found 19 papers, 6 papers with code
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?
Automatic Speech Recognition
Speech-to-Speech Translation
+3
Towards Being Parameter-Efficient: A Stratified Sparsely Activated Transformer with Dynamic Capacity
Mixture-of-experts (MoE) models that employ sparse activation have demonstrated effectiveness in significantly increasing the number of parameters while maintaining low computational requirements per token.
Language-Aware Multilingual Machine Translation with Self-Supervised Learning
In this work, we first investigate how to utilize intra-distillation to learn more *language-specific* parameters and then show the importance of these language-specific parameters.
Toxicity in Multilingual Machine Translation at Scale
We evaluate and analyze added toxicity when translating a large evaluation dataset (HOLISTICBIAS, over 472k sentences, covering 13 demographic axes) from English into 164 languages.
No Language Left Behind: Scaling Human-Centered Machine Translation
Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today.
Ranked #1 on
Machine Translation
on IWSLT2015 English-Vietnamese
(SacreBLEU metric)
Text normalization for endangered languages: the case of Ligurian
Text normalization is a crucial technology for low-resource languages which lack rigid spelling conventions.
OCR Improves Machine Translation for Low-Resource Languages
We aim to investigate the performance of current OCR systems on low resource languages and low resource scripts.
Multi-task Retrieval for Knowledge-Intensive Tasks
Retrieving relevant contexts from a large corpus is a crucial step for tasks such as open-domain question answering and fact checking.
Conversational Semantic Parsing
In this paper, we propose a semantic representation for such task-oriented conversational systems that can represent concepts such as co-reference and context carryover, enabling comprehensive understanding of queries in a session.
KILT: a Benchmark for Knowledge Intensive Language Tasks
We test both task-specific and general baselines, evaluating downstream performance in addition to the ability of the models to provide provenance.
Ranked #2 on
Open-Domain Question Answering
on KILT: TriviaQA
Decoding Brain Activity Associated with Literal and Metaphoric Sentence Comprehension Using Distributional Semantic Models
We evaluate a range of semantic models (word embeddings, compositional, and visual models) in their ability to decode brain activity associated with reading of both literal and metaphoric sentences.
Modeling Affirmative and Negated Action Processing in the Brain with Lexical and Compositional Semantic Models
Recent work shows that distributional semantic models can be used to decode patterns of brain activity associated with individual words and sentence meanings.
Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing
Latent tree learning models represent sentences by composing their words according to an induced parse tree, all based on a downstream task.
Jointly Learning Sentence Embeddings and Syntax with Unsupervised Tree-LSTMs
It can therefore be seen as a tree-based RNN that is unsupervised with respect to the parse trees.
