Current NLP systems impress us by achieving close-to-human performance on benchmarks of answering commonsense questions or writing interesting stories. However, most of the progress is evaluated using static, closed-ended datasets created for individual tasks. To deploy commonsense reasoning services in the wild, we look to develop and evaluate systems that can generate answers in an open-ended way, perform robust logical reasoning, and generalize across diverse task formats, domains, and datasets. In this talk I will share our effort on introducing new formulations of commonsense reasoning challenges and novel evaluation protocols, towards broadening the scope in approaching machine common sense. We hope that such a shift of evaluation paradigm would encourage more research on externalizing the model reasoning process and improving model robustness and cross-task generalization.

Xiang Ren is an assistant professor and Viterbi Early Career Chair at the USC Computer Science Department, a Research Team Leader at USC ISI, and the director of the Intelligence and Knowledge Discovery (INK) Lab at USC. Priorly, he spent time as a research scholar at Stanford University and received his Ph.D. in Computer Science from the University of Illinois Urbana-Champaign. Ren's research seeks to build generalizable natural language processing (NLP) systems which can handle a wide variety of language tasks and situations. He works on new algorithms and datasets to make NLP systems cheaper to develop and maintain, arm machine models with common sense, and improve models’ transparency and reliability to build user trust. His research work has received several best paper awards in top NLP and AI conference venues. Ren has been awarded a NSF CAREER Award, multiple faculty research awards from Google, Facebook, Amazon, JP Morgan and Sony, and the 2018 ACM SIGKDD Doctoral Dissertation Award. He was named Forbes' Asia 30 Under 30 in 2019.

The development of advanced spoken language technologies based on automatic speech recognition (ASR) and text-to-speech synthesis (TTS) has enabled computers to either learn how to listen or speak. Many applications and services are now available but still support fewer than 100 languages. Nearly 7000 living languages that are spoken by 350 million people remain uncovered. This is because the construction is commonly done based on machine learning trained in a supervised fashion where a large amount of paired speech and corresponding transcription is required. In this talk, we will introduce a semi-supervised learning mechanism based on a machine speech chain framework. First, we describe the primary machine speech chain architecture that learns not only to listen or speak but also to listen while speaking. The framework enables ASR and TTS to teach each other given unpaired data. After that, we describe the use of machine speech chain for code-switching and cross-lingual ASR and TTS of several languages, including low-resourced ethnic languages. Finally, we describe the recent multimodal machine chain that mimics overall human communication to listen while speaking and visualizing. With the support of image captioning and production models, the framework enables ASR and TTS to improve their performance using an image-only dataset.

Sakriani Sakti is currently an associate professor at Japan Advanced Institute of Science and Technology (JAIST) Japan, adjunct associate professor at Nara Institute of Science and Technology (NAIST) Japan, visiting research scientist at RIKEN Center for Advanced Intelligent Project (RIKEN AIP) Japan, and adjunct professor at the University of Indonesia. She received DAAD-Siemens Program Asia 21st Century Award in 2000 to study in Communication Technology, University of Ulm, Germany, and received her MSc degree in 2002. During her thesis work, she worked with the Speech Understanding Department, DaimlerChrysler Research Center, Ulm, Germany. She then worked as a researcher at ATR Spoken Language Communication (SLC) Laboratories Japan in 2003-2009, and NICT SLC Groups Japan in 2006-2011, which established multilingual speech recognition for speech-to-speech translation. While working with ATR and NICT, Japan, she continued her study (2005-2008) with Dialog Systems Group University of Ulm, Germany, and received her Ph.D. degree in 2008. She was actively involved in international collaboration activities such as Asian Pacific Telecommunity Project (2003-2007) and various speech-to-speech translation research projects, including A-STAR and U-STAR (2006-2011). In 2011-2017, she was an assistant professor at the Augmented Human Communication Laboratory, NAIST, Japan. She also served as a visiting scientific researcher of INRIA Paris-Rocquencourt, France, in 2015-2016, under JSPS Strategic Young Researcher Overseas Visits Program for Accelerating Brain Circulation. In 2018–2021, she was a research associate professor at NAIST and a research scientist at RIKEN, Center for Advanced Intelligent Project AIP, Japan. Currently, she is an associate professor at JAIST, adjunct associate professor at NAIST, visiting research scientist at RIKEN AIP, and adjunct professor at the University of Indonesia. She is a member of JNS, SFN, ASJ, ISCA, IEICE, and IEEE. Furthermore, she is currently a committee member of IEEE SLTC (2021-2023) and an associate editor of the IEEE/ACM Transactions on Audio, Speech, and Language Processing (2020-2023). She was a board member of Spoken Language Technologies for Under-resourced languages (SLTU) and the general chair of SLTU2016. She was also the general chair of the "Digital Revolution for Under-resourced Languages (DigRevURL)" Workshop as the Interspeech Special Session in 2017 and DigRevURL Asia in 2019. She was also the organizing committee of the Zero Resource Speech Challenge 2019 and 2020. She was also involved in creating joint ELRA and ISCA Special Interest Group on Under-resourced Languages (SIGUL) and served as SIGUL Board since 2018. Last year, in collaboration with UNESCO and ELRA, she was also the organizing committee of the International Conference of "Language Technologies for All (LT4All): Enabling Linguistic Diversity and Multilingualism Worldwide". Her research interests lie in deep learning & graphical model framework, statistical pattern recognition, zero-resourced speech technology, multilingual speech recognition and synthesis, spoken language translation, social-affective dialog system, and cognitive-communication.

The design of widespread vision-and-language datasets and pre-trained encoders directly adopts, or draws inspiration from, the concepts and images of ImageNet. While one can hardly overestimate how much this benchmark contributed to progress in computer vision, it is mostly derived from lexical databases and image queries in English, resulting in source material with a North American or Western European bias. Therefore, we devise a new protocol to construct an ImageNet-style hierarchy representative of more languages and cultures. In particular, we let the selection of both concepts and images be entirely driven by native speakers, rather than scraping them automatically. Specifically, we focus on a typologically diverse set of languages, namely, Indonesian, Mandarin Chinese, Swahili, Tamil, and Turkish. On top of the concepts and images obtained through this new protocol, we create a multilingual dataset for Multicultural Reasoning over Vision and Language (MaRVL) by eliciting statements from native speaker annotators about pairs of images. The task consists of discriminating whether each grounded statement is true or false. We establish a series of baselines using state-of-the-art models and find that their cross-lingual transfer performance lags dramatically behind supervised performance in English. These results invite us to reassess the robustness and accuracy of current state-of-the-art models beyond a narrow domain, but also open up new exciting challenges for the development of truly multilingual and multicultural systems.

Desmond is an Assistant Professor at the University of Copenhagen. His primary research interests are multimodal and multilingual machine learning and he was involved in the creation of the Multi30K, How2, and MaRVL datasets. His work received an Area Chair Favourite paper at COLING 2018 and the Best Long Paper Award at EMNLP 2021. He co-organised the Multimodal Machine Translation Shared Task from 2016–2018, the 2018 Frederick Jelinek Memorial Workshop on Grounded Sequence-to-Sequence Learning, the How2 Challenge Workshop at ICML 2019, and the Workshop on Multilingual Multimodal Learning at ACL 2022.