In this work, we propose a computational framework that leverages existing out-of-language data to create a conversational agent for the delivery of Self-Attachment Technique (SAT) in Mandarin. Our framework does not require large-scale human translations, yet it achieves a comparable performance whilst also maintaining safety and reliability. We propose two different methods of augmenting available response data through empathetic rewriting. We evaluate our chatbot against a previous, English-only SAT chatbot through non-clinical human trials (N=42), each lasting five days, and quantitatively show that we are able to attain a comparable level of performance to the English SAT chatbot. We provide qualitative analysis on the limitations of our study and suggestions with the aim of guiding future improvements.
General purpose relation extraction has recently seen considerable gains in part due to a massively data-intensive distant supervision technique from Soares et al. (2019) that produces state-of-the-art results across many benchmarks. In this work, we present a methodology for collecting high quality training data for relation extraction from unlabeled text that achieves a near-recreation of their zero-shot and few-shot results at a fraction of the training cost. Our approach exploits the predictable distributional structure of date-marked news articles to build a denoised corpus -- the extract…
SoT: Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models
Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education（大数据与人工智能交通 key laboratory（北京交通大学））；School of Computer Science and Technology, Beijing Jiaotong University（计算机科学与技术学院（北京交通大学））；University of Montreal（蒙特利尔大学）
We release a multilingual neural machine translation model, which can be used to translate text in the biomedical domain. The model can translate from 5 languages (French, German, Italian, Korean and Spanish) into English. It is trained with large amounts of generic and biomedical data, using domain tags. Our benchmarks show that it performs near state-of-the-art both on news (generic domain) and biomedical test sets, and that it outperforms the existing publicly released models. We believe that this release will help the large-scale multilingual analysis of the digital content of the COVID-19…
Dense retrieval models using a transformer-based bi-encoder design have emerged as an active area of research. In this work, we focus on the task of monolingual retrieval in a variety of typologically diverse languages using one such design. Although recent work with multilingual transformers demonstrates that they exhibit strong cross-lingual generalization capabilities, there remain many open research questions, which we tackle here. Our study is organized as a "best practices" guide for training multilingual dense retrieval models, broken down into three main scenarios: where a multilingual…
Mitigating Language-Level Performance Disparity in mPLMs via Teacher Language Selection and Cross-lingual Self-Distillation
National Key Laboratory for Multimedia Information Processing, Peking University（北京大学多媒体信息处理国家重点实验室）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；Collaborative Innovation Center for Language Ability（语言能力协同创新中心）
University of California, Santa Barbara（加州大学圣巴巴拉分校）；ByteDance AI Lab（字节跳动人工智能实验室）
We present a new large-scale multilingual video description dataset, VATEX, which contains over 41,250 videos and 825,000 captions in both English and Chinese. Among the captions, there are over 206,000 English-Chinese parallel translation pairs. Compared to the widely-used MSR-VTT dataset, VATEX is multilingual, larger, linguistically complex, and more diverse in terms of both video and natural language descriptions. We also introduce two tasks for video-and-language research based on VATEX: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact uni…
ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus
New York University Abu Dhabi（纽约大学阿布扎比分校）；University of Stuttgart（斯图加特大学）；University of Tübingen（蒂宾根大学）；United Arab Emirates University（阿联酋大学）；Zayed University（扎耶德大学）
University of the Basque Country UPV/EHU（巴斯克大学UPV/EHU）
The lack of wide coverage datasets annotated with everyday metaphorical expressions for languages other than English is striking. This means that most research on supervised metaphor detection has been published only for that language. In order to address this issue, this work presents the first corpus annotated with naturally occurring metaphors in Spanish large enough to develop systems to perform metaphor detection. The presented dataset, CoMeta, includes texts from various domains, namely, news, political discourse, Wikipedia and reviews. In order to label CoMeta, we apply the MIPVU method…
CM-Align: Consistency-based Multilingual Alignment for Large Language Models
Key Laboratory of Big Data & Artificial Intelligence in Transportation, Beijing Jiaotong University, Ministry of Education（大数据与人工智能交通运输 key laboratory，北京交通大学，教育部）；School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China（计算机科学与技术学院，北京交通大学，北京，中国）；Pattern Recognition Center, WeChat AI, Tencent Inc, China（模式识别中心，微信AI，腾讯公司，中国）
How Good is Your Wikipedia? Auditing Data Quality for Low-resource and Multilingual NLP
Department of Computer Science, KU Leuven（库勒万大学计算机科学系）；CENTAL, UCLouvain（UCLouvain的CENTAL）；Department of Computer Science, Aalborg University（奥尔堡大学计算机科学系）；Department of Computer and Information Science, Linköping University（利厄浦大学计算机与信息科学系）；Google Translate（谷歌翻译）
本文审计非英语 Wikipedia 的数据质量，通过多种过滤技术发现短文章与重复问题，并证明质量剪枝可在不降低性能的前提下提升低资源多语言 NLP 的训练效率。
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Meta AI
With multilingual machine translation (MMT) models continuing to grow in size and number of supported languages, it is natural to reuse and upgrade existing models to save computation as data becomes available in more languages. However, adding new languages requires updating the vocabulary, which complicates the reuse of embeddings. The question of how to reuse existing models while also making architectural changes to provide capacity for both old and new languages has also not been closely studied. In this work, we introduce three techniques that help speed up effective learning of the new …
Why Vision Fails as a Universal Bridge: Rectifying Modality Asynchrony in Multilingual MLLMs
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Loop Area Institute（深圳河套学院）；National Health Data Institute (Shenzhen)（国家健康数据研究院（深圳））
X-MuTeST: A Multilingual Benchmark for Explainable Hate Speech Detection and A Novel LLM-consulted Explanation Framework
Indian Institute of Technology Indore（印度理工学院Indore）；Indian Institute of Information Technology Dharwad（印度信息科技学院Dharwad）；Arizona State University（亚利桑那州立大学）；Indian Institute of Technology Mandi（印度理工学院Mandi）
Department of Computational Linguistics, University of Zurich（苏黎世大学计算语言学系）
While several benefits were realized for multilingual vision-language pretrained models, recent benchmarks across various tasks and languages showed poor cross-lingual generalisation when multilingually pre-trained vision-language models are applied to non-English data, with a large gap between (supervised) English performance and (zero-shot) cross-lingual transfer. In this work, we explore the poor performance of these models on a zero-shot cross-lingual visual question answering (VQA) task, where models are fine-tuned on English visual-question data and evaluated on 7 typologically diverse l…
Heidelberg Institute for Theoretical Studies（海德堡理论研究所）；Technische Universität Darmstadt（达姆施塔特工业大学）；Universität Bielefeld（比勒费尔德大学）
Multilingual representations pre-trained with monolingual data exhibit considerably unequal task performances across languages. Previous studies address this challenge with resource-intensive contextualized alignment, which assumes the availability of large parallel data, thereby leaving under-represented language communities behind. In this work, we attribute the data hungriness of previous alignment techniques to two limitations: (i) the inability to sufficiently leverage data and (ii) these techniques are not trained properly. To address these issues, we introduce supervised and unsupervise…
