We hypothesize that existing sentence-level machine translation (MT) metrics become less effective when the human reference contains ambiguities. To verify this hypothesis, we present a very simple method for extending pretrained metrics to incorporate context at the document level. We apply our method to three popular metrics, BERTScore, Prism, and COMET, and to the reference free metric COMET-QE. We evaluate the extended metrics on the WMT 2021 metrics shared task using the provided MQM annotations. Our results show that the extended metrics outperform their sentence-level counterparts in about 85% of the tested conditions, when excluding results on low-quality human references. Additionally, we show that our document-level extension of COMET-QE dramatically improves its accuracy on discourse phenomena tasks, outperforming a dedicated baseline by up to 6.1%. Our experimental results support our initial hypothesis and show that a simple extension of the metrics permits them to take advantage of context to resolve ambiguities in the reference.

Global teams frequently consist of language-based subgroups who put together complementary information to achieve common goals. Previous research outlines a two-step work communication flow in these teams. There are team meetings using a required common language (i.e., English); in preparation for those meetings, people have subgroup conversations in their native languages. Work communication at team meetings is often less effective than in subgroup conversations. In the current study, we investigate the idea of leveraging machine translation (MT) to facilitate global team meetings. We hypothesize that exchanging subgroup conversation logs before a team meeting offers contextual information that benefits teamwork at the meeting. MT can translate these logs, which enables comprehension at a low cost. To test our hypothesis, we conducted a between-subjects experiment where twenty quartets of participants performed a personnel selection task. Each quartet included two English native speakers (NS) and two non-native speakers (NNS) whose native language was Mandarin. All participants began the task with subgroup conversations in their native languages, then proceeded to team meetings in English. We manipulated the exchange of subgroup conversation logs prior to team meetings: with MT-mediated exchanges versus without. Analysis of participants' subjective experience, task performance, and depth of discussions as reflected through their conversational moves jointly indicates that team meeting quality improved when there were MT-mediated exchanges of subgroup conversation logs as opposed to no exchanges. We conclude with reflections on when and how MT could be applied to enhance global teamwork across a language barrier.

Machine Translation System (MTS) serves as an effective tool for communication by translating text or speech from one language to another language. The need of an efficient translation system becomes obvious in a large multilingual environment like India, where English and a set of Indian Languages (ILs) are officially used. In contrast with English, ILs are still entreated as low-resource languages due to unavailability of corpora. In order to address such asymmetric nature, multilingual neural machine translation (MNMT) system evolves as an ideal approach in this direction. In this paper, we propose a MNMT system to address the issues related to low-resource language translation. Our model comprises of two MNMT systems i.e. for English-Indic (one-to-many) and the other for Indic-English (many-to-one) with a shared encoder-decoder containing 15 language pairs (30 translation directions). Since most of IL pairs have scanty amount of parallel corpora, not sufficient for training any machine translation model. We explore various augmentation strategies to improve overall translation quality through the proposed model. A state-of-the-art transformer architecture is used to realize the proposed model. Trials over a good amount of data reveal its superiority over the conventional models. In addition, the paper addresses the use of language relationships (in terms of dialect, script, etc.), particularly about the role of high-resource languages of the same family in boosting the performance of low-resource languages. Moreover, the experimental results also show the advantage of backtranslation and domain adaptation for ILs to enhance the translation quality of both source and target languages. Using all these key approaches, our proposed model emerges to be more efficient than the baseline model in terms of evaluation metrics i.e BLEU (BiLingual Evaluation Understudy) score for a set of ILs.

Neural metrics have achieved impressive correlation with human judgements in the evaluation of machine translation systems, but before we can safely optimise towards such metrics, we should be aware of (and ideally eliminate) biases toward bad translations that receive high scores. Our experiments show that sample-based Minimum Bayes Risk decoding can be used to explore and quantify such weaknesses. When applying this strategy to COMET for en-de and de-en, we find that COMET models are not sensitive enough to discrepancies in numbers and named entities. We further show that these biases are hard to fully remove by simply training on additional synthetic data and release our code and data for facilitating further experiments.

As the development of the encoder-decoder architecture, researchers are able to study the text generation tasks with broader types of data. Among them, KB-to-text aims at converting a set of knowledge triples into human readable sentences. In the original setting, the task assumes that the input triples and the text are exactly aligned in the perspective of the embodied knowledge/information. In this paper, we extend this setting and explore how to facilitate the trained model to generate more informative text, namely, containing more information about the triple entities but not conveyed by the input triples. To solve this problem, we propose a novel memory augmented generator that employs a memory network to memorize the useful knowledge learned during the training and utilizes such information together with the input triples to generate text in the operational or testing phase. We derive a dataset from WebNLG for our new setting and conduct extensive experiments to investigate the effectiveness of our model as well as uncover the intrinsic characteristics of the setting.

Cross-lingual transfer learning has proven useful in a variety of Natural Language Processing (NLP) tasks, but it is understudied in the context of legal NLP, and not at all in Legal Judgment Prediction (LJP). We explore transfer learning techniques on LJP using the trilingual Swiss-Judgment-Prediction dataset, including cases written in three languages. We find that cross-lingual transfer improves the overall results across languages, especially when we use adapter-based fine-tuning. Finally, we further improve the model's performance by augmenting the training dataset with machine-translated versions of the original documents, using a 3x larger training corpus. Further on, we perform an analysis exploring the effect of cross-domain and cross-regional transfer, i.e., train a model across domains (legal areas), or regions. We find that in both settings (legal areas, origin regions), models trained across all groups perform overall better, while they also have improved results in the worst-case scenarios. Finally, we report improved results when we ambitiously apply cross-jurisdiction transfer, where we further augment our dataset with Indian legal cases.

Artificial intelligence is a discipline that attempts to simulate human intelligence. The field of AI covers a wide range of technologies, from the relatively simple to the more complex. This wide range of technologies enables AI to solve a wide range of problems, from automated machine translation to high-level reasoning. These technologies include Machine Learning, Natural Language Processing, Knowledge Representation, Probabilistic Reasoning, Logic Programming, Expert Systems, and Genetic Programming. The complexity of AI is often misunderstood by those who have never worked in the field.

A self-driving car must be able to reliably handle adverse weather conditions (e.g., snowy) to operate safely. In this paper, we investigate the idea of turning sensor inputs (i.e., images) captured in an adverse condition into a benign one (i.e., sunny), upon which the downstream tasks (e.g., semantic segmentation) can attain high accuracy. Prior work primarily formulates this as an unpaired image-to-image translation problem due to the lack of paired images captured under the exact same camera poses and semantic layouts. While perfectly-aligned images are not available, one can easily obtain coarsely-paired images. For instance, many people drive the same routes daily in both good and adverse weather; thus, images captured at close-by GPS locations can form a pair. Though data from repeated traversals are unlikely to capture the same foreground objects, we posit that they provide rich contextual information to supervise the image translation model. To this end, we propose a novel training objective leveraging coarsely-aligned image pairs. We show that our coarsely-aligned training scheme leads to a better image translation quality and improved downstream tasks, such as semantic segmentation, monocular depth estimation, and visual localization.

Domain adaptation is an important challenge for neural machine translation. However, the traditional fine-tuning solution requires multiple extra training and yields a high cost. In this paper, we propose a non-tuning paradigm, resolving domain adaptation with a prompt-based method. Specifically, we construct a bilingual phrase-level database and retrieve relevant pairs from it as a prompt for the input sentences. By utilizing Retrieved Phrase-level Prompts (RePP), we effectively boost the translation quality. Experiments show that our method improves domain-specific machine translation for 6.2 BLEU scores and improves translation constraints for 11.5% accuracy without additional training.

This paper introduces a new data augmentation method for neural machine translation that can enforce stronger semantic consistency both within and across languages. Our method is based on Conditional Masked Language Model (CMLM) which is bi-directional and can be conditional on both left and right context, as well as the label. We demonstrate that CMLM is a good technique for generating context-dependent word distributions. In particular, we show that CMLM is capable of enforcing semantic consistency by conditioning on both source and target during substitution. In addition, to enhance diversity, we incorporate the idea of soft word substitution for data augmentation which replaces a word with a probabilistic distribution over the vocabulary. Experiments on four translation datasets of different scales show that the overall solution results in more realistic data augmentation and better translation quality. Our approach consistently achieves the best performance in comparison with strong and recent works and yields improvements of up to 1.90 BLEU points over the baseline.