Since its inception in 1962, the Association for Computational Linguistics (ACL) has been “the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics (CL) or natural language processing (NLP).”
ACL activities include the annual summer meeting and publication (via MIT Press) of the Computational Linguistics journal.The 60th Annual Meeting of the ACL took place from May 22–27, 2022 as a hybrid event, in Dublin and online.
These were the Top 10 themes in this year’s conference.
Summarization, low-resource languages, speech technologies, multimodality, and ethics were also covered, albeit to a lesser extent.
More than 2,000 authors from all over the world contributed to the conference, either via long or short paper (604 long papers, 98 short papers).
Best Paper Award for 2022
Learned Incremental Representations for Parsing
Nikita Kitaev, PhD student; Thomas Lu, undergraduate student, UC Berkeley; Dan Klein, Technical Fellow, Microsoft Semantic Machines and Professor, UC Berkeley
The authors of this paper designed a representation for parsing that is “maximally speculation free.” As they noted, “human capabilities suggest that we should also be able to build accurate parsers that […] operate incrementally.”
Best Special Theme Paper Award
Requirements and Motivations of Low-Resource Speech Synthesis for Language Revitalization
Aidan Pine and Patrick William Littell, National Research Council Canada; Dan Wells, PhD student and Korin Richmond, Associate Professor, University of Edinburgh; Nathan Brinklow, Professor, Queen’s University
The authors aimed to revitalize three indigenous languages by developing speech synthesis systems for them, reevaluating the question of “how much data is required to build low-resource speech synthesis systems featuring state-of-the-art neural models.”
Best Resource Paper Award
DiBiMT: A Novel Benchmark for Measuring Word Sense Disambiguation Biases in Machine Translation
Niccolò Campolungo, Federico Martelli, and Roberto Navigli, Sapienza University of Rome; Francesco Saina, SSML Carlo Bo
This paper highlights the importance of bias in machine translation (MT). DiBiMT, is a “novel benchmark for measuring and understanding semantic biases in NMT, which goes beyond simple accuracy and provides novel metrics that summarize how biased NMT models are.”
Best Linguistic Insight Paper Award
KinyaBERT: a Morphology-aware Kinyarwanda Language Model
Antoine Nzeyimana, University of Massachusetts Amherst; Andre Niyongabo Rubungo, Polytechnic University of Catalonia
This paper demonstrated the effectiveness of explicitly incorporating morphological information in language-model pretraining. The authors proposed a two-tier BERT architecture (first tier encodes morphological information; second one, sentence-level information) and evaluated it on the low-resource, morphologically rich Kinyarwanda language. The authors said this work “should motivate more research into morphology-aware language models.”
The richness and quality of the conference papers is reflected in the Outstanding Papers list as judged by the Best Paper Committee.
To celebrate its 60th year, ACL launched the 60-60 Initiative as an effort to “remove the ingrained linguistic bias in the scientific landscape in general and CL science in particular.”
The inauguration of this Diversity & Inclusion Special Initiative has already been embraced by a core group comprising academic teams from across the globe (National University of Singapore, Yale University, University of Illinois Urbana Champaign, NYUAD, and King Saud University), big tech (Baidu, Meta), medium tech (AppTek) companies, non-profit organizations (AI2), startups (aiXplain), as well as annotation companies (YaiGlobal).
The 60-60 Initiative features some strategic milestones to be reached by 2023, including
Volunteers are encouraged to join this initiative, which aims to democratize CL sciences and maximize CL global reach.
