33 citations · 68 across the 9 of their papers we have counts for
9 papers
IndicQE-APE: A Consolidated Benchmark for Quality Estimation and Automatic Post-Editing for Indic Languages
Diptesh Kanojia, Archchana Sindhujan, Sourabh Deoghare +14
Indic quality estimation (QE) and automatic post-editing (APE) data is spread across separate releases, so no single resource supports training and evaluation across tasks and lang…
Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation
Patrick Fernandes, Aman Madaan, Emmy Liu +8
Many recent advances in natural language generation have been fueled by training large language models on internet-scale data. However, this paradigm can lead to models that genera…
Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages
Ayyoob Imani, Peiqin Lin, Amir Hossein Kargaran +8
The NLP community has mainly focused on scaling Large Language Models (LLMs) vertically, i.e., making them better for about 100 languages. We instead scale LLMs horizontally: we cr…
Python Code Generation by Asking Clarification Questions
Haau-Sing Li, Mohsen Mesgar, André F. T. Martins +1
Code generation from text requires understanding the user's intent from a natural language description and generating an executable code snippet that satisfies this intent. While r…
Improving abstractive summarization with energy-based re-ranking
Diogo Pernes, Afonso Mendes, André F. T. Martins
Current abstractive summarization systems present important weaknesses which prevent their deployment in real-world applications, such as the omission of relevant information and t…
CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task
Ricardo Rei, Marcos Treviso, Nuno M. Guerreiro +9
We present the joint contribution of IST and Unbabel to the WMT 2022 Shared Task on Quality Estimation (QE). Our team participated on all three subtasks: (i) Sentence and Word-leve…
Efficient Methods for Natural Language Processing: A Survey
Marcos Treviso, Ji-Ung Lee, Tianchu Ji +19
Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance mea…
Quality-Aware Decoding for Neural Machine Translation
Patrick Fernandes, António Farinhas, Ricardo Rei +4
Despite the progress in machine translation quality estimation and evaluation in the last years, decoding in neural machine translation (NMT) is mostly oblivious to this and center…
When Does Translation Require Context? A Data-driven, Multilingual Exploration
Patrick Fernandes, Kayo Yin, Emmy Liu +2
Although proper handling of discourse significantly contributes to the quality of machine translation (MT), these improvements are not adequately measured in common translation qua…