Automatic Reference-Based Evaluation of Pronoun Translation Misses the Point

We compare the performance of the APT and AutoPRF metrics for pronoun translation against a manually annotated dataset comprising human judgements as to the correctness of translations of the PROTEST test suite. Although there is some correlation with the human judgements, a range of issues limit the performance of the automated metrics. Instead, we recommend the use of semi-automatic metrics and test suites in place of fully automatic metrics.Comment: EMNLP 201

Arabic and English Relative Clauses and Machine Translation Challenges

The study aims at performing an error analysis as well as providing an evaluation of the quality of neural machine translation (NMT) represented by Google Translate when translating relative clauses. The study uses two test suites are composed of sentences that contain relative clauses. The first test suite composes of 108 pair sentences that are translated from English to Arabic whereas the second composes of 72 Arabic sentences that are translated into English. Errors annotation is performed by 6 professional annotators. The study presents a list of the annotated errors divided into accuracy and fluency errors that occur based on MQM. Manual evaluation is also performed by the six professionals along with a BLEU automatic evaluation using the Tilde Me platform. The results show that fluency errors are more frequent than accuracy errors. They also show that the frequency of errors and MT quality when translating from English into Arabic is lower than the frequency of errors and MT quality when translating from Arabic into English is also presented. Based on the performed error analysis and both manual and automatic evaluation, it is pointed out that the gap between MT and professional human translation is still large

Toward Gender-Inclusive Coreference Resolution

Correctly resolving textual mentions of people fundamentally entails making inferences about those people. Such inferences raise the risk of systemic biases in coreference resolution systems, including biases that can harm binary and non-binary trans and cis stakeholders. To better understand such biases, we foreground nuanced conceptualizations of gender from sociology and sociolinguistics, and develop two new datasets for interrogating bias in crowd annotations and in existing coreference resolution systems. Through these studies, conducted on English text, we confirm that without acknowledging and building systems that recognize the complexity of gender, we build systems that lead to many potential harms.Comment: 28 pages; ACL versio