Search Results for author: Francisco Guzman

Found 14 papers, 3 papers with code

Consistent Human Evaluation of Machine Translation across Language Pairs

no code implementations AMTA 2022 Daniel Licht, Cynthia Gao, Janice Lam, Francisco Guzman, Mona Diab, Philipp Koehn

Obtaining meaningful quality scores for machine translation systems through human evaluation remains a challenge given the high variability between human evaluators, partly due to subjective expectations for translation quality for different language pairs.

Machine Translation Translation

Alternative Input Signals Ease Transfer in Multilingual Machine Translation

no code implementations ACL 2022 Simeng Sun, Angela Fan, James Cross, Vishrav Chaudhary, Chau Tran, Philipp Koehn, Francisco Guzman

Further, we find that incorporating alternative inputs via self-ensemble can be particularly effective when training set is small, leading to +5 BLEU when only 5% of the total training data is accessible.

Machine Translation Translation

Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning

1 code implementation12 Jul 2021 Jun Wang, Chang Xu, Francisco Guzman, Ahmed El-Kishky, Yuqing Tang, Benjamin I. P. Rubinstein, Trevor Cohn

Neural machine translation systems are known to be vulnerable to adversarial test inputs, however, as we show in this paper, these systems are also vulnerable to training attacks.

Data Poisoning Machine Translation +3

A Targeted Attack on Black-Box Neural Machine Translation with Parallel Data Poisoning

no code implementations2 Nov 2020 Chang Xu, Jun Wang, Yuqing Tang, Francisco Guzman, Benjamin I. P. Rubinstein, Trevor Cohn

In this paper, we show that targeted attacks on black-box NMT systems are feasible, based on poisoning a small fraction of their parallel training data.

Data Poisoning Machine Translation +2

Pairwise Neural Machine Translation Evaluation

no code implementations IJCNLP 2015 Francisco Guzman, Shafiq Joty, Lluis Marquez, Preslav Nakov

We present a novel framework for machine translation evaluation using neural networks in a pairwise setting, where the goal is to select the better translation from a pair of hypotheses, given the reference translation.

Machine Translation Sentence +2

DiscoTK: Using Discourse Structure for Machine Translation Evaluation

no code implementations WS 2014 Shafiq Joty, Francisco Guzman, Lluis Marquez, Preslav Nakov

We present novel automatic metrics for machine translation evaluation that use discourse structure and convolution kernels to compare the discourse tree of an automatic translation with that of the human reference.

Machine Translation Translation

CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs

no code implementations EMNLP 2020 Ahmed El-Kishky, Vishrav Chaudhary, Francisco Guzman, Philipp Koehn

We mine sixty-eight snapshots of the Common Crawl corpus and identify web document pairs that are translations of each other.

The AMARA Corpus: Building Parallel Language Resources for the Educational Domain

no code implementations LREC 2014 Ahmed Abdelali, Francisco Guzman, Hassan Sajjad, Stephan Vogel

This paper presents the AMARA corpus of on-line educational content: a new parallel corpus of educational video subtitles, multilingually aligned for 20 languages, i. e. 20 monolingual corpora and 190 parallel corpora.

Machine Translation Translation

Cannot find the paper you are looking for? You can Submit a new open access paper.