Translation quality estimation (QE) is an important component in real-world machine translation applications. Unfortunately, human labeled QE datasets, which play an important role in developing and assessing QE models, are only available for limited language pairs. In this paper, we present the first English-Persian QE dataset, called EPOQUE, which has manually annotated direct assessment labels. EPOQUE contains 1000 sentences translated from English to Persian and annotated by three human annotators. It is publicly available, and thus can be used as a zero-shot test set, or for other scenarios in future work. We also evaluate and report the performance of two state-of-the-art QE models, i.e., Transquest and CometKiwi, as baselines on our dataset. Furthermore, our experiments show that using a small subset of the proposed dataset containing 300 sentences to fine-tune Transquest, can improve its performance by more that 8% in terms of the Pearson correlation with a held-out test set.

Neural machine translation requires large number of parallel sentences along with in-domain parallel data to attain best results. Nevertheless, no scientific parallel corpus for English-Persian language pair is available. In this paper, a parallel corpus called Esposito is introduced, which contains 3.5 million parallel sentences in the scientific domain for English-Persian language pair. In addition, we present a manually validated scientific test set that might serve as a baseline for future studies. We show that a system trained using Esposito along with other publicly available data improves the baseline on average by 7.6 and 8.4 BLEU scores for En->Fa and Fa->En directions, respectively. Additionally, domain analysis using the 5-gram KenLM model revealed notable distinctions between our parallel corpus and the existing generic parallel corpus. This dataset will be available to the public upon the acceptance of the paper.

Word alignment has many applications including cross-lingual annotation projection, bilingual lexicon extraction, and the evaluation or analysis of translation outputs. Recent studies show that using contextualized embeddings from pre-trained multilingual language models could give us high quality word alignments without the need of parallel training data. In this work, we propose PMI-Align which computes and uses the point-wise mutual information between source and target tokens to extract word alignments, instead of the cosine similarity or dot product which is mostly used in recent approaches. Our experiments show that our proposed PMI-Align approach could outperform the rival methods on five out of six language pairs. Although our approach requires no parallel training data, we show that this method could also benefit the approaches using parallel data to fine-tune pre-trained language models on word alignments. Our code and data are publicly available.

Simultaneous translation on both text and speech focuses on a real-time and low-latency scenario where the model starts translating before reading the complete source input. Evaluating simultaneous translation models is more complex than offline models because the latency is another factor to consider in addition to translation quality. The research community, despite its growing focus on novel modeling approaches to simultaneous translation, currently lacks a universal evaluation procedure. Therefore, we present SimulEval, an easy-to-use and general evaluation toolkit for both simultaneous text and speech translation. A server-client scheme is introduced to create a simultaneous translation scenario, where the server sends source input and receives predictions for evaluation and the client executes customized policies. Given a policy, it automatically performs simultaneous decoding and collectively reports several popular latency metrics. We also adapt latency metrics from text simultaneous translation to the speech task. Additionally, SimulEval is equipped with a visualization interface to provide better understanding of the simultaneous decoding process of a system. SimulEval has already been extensively used for the IWSLT 2020 shared task on simultaneous speech translation. Code will be released upon publication.