United States

NAACL 2022

Unifying Parsing and Tree-Structured Models for Generating Sentence Semantic Representations

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

poster

NLP models trained on text have been shown to reproduce human stereotypes, which can magnify harms to marginalized groups when systems are deployed at scale. We adapt the Agency-Belief-Communion (ABC) stereotype model of Koch et al. (2016) from social psychology as a framework for the systematic study and discovery of stereotypic group-trait associations in language models (LMs). We introduce the sensitivity test (SeT) for measuring stereotypical associations from language models. To evaluate SeT and other measures using the ABC model, we collect group-trait judgments from U.S.-based subjects to compare with English LM stereotypes. Finally, we extend this framework to measure LM stereotyping
of intersectional identities.

Theory-Grounded Measurement of U.S. Social Stereotypes in English Language Models

Defending Compositionality in Emergent Languages

Exploring the Effect of Dialect Mismatched Language Models in Telugu Automatic Speech Recognition

What “Drives” the Use of Metaphorical Language? Negative Insights from Abstractness, Affect, Discourse Coherence and Contextualized Word Representations

Preschool Children Speech Recognition for Early Childhood Intervention: Motivation and Challenges

Machine Narrative Comprehension in Fictional Characters Personality Prediction Task

Divide & Conquer for Entailment-aware Multi-hop Evidence Retrieval

Neural Retriever and Go Beyond: A Thesis Proposal

Towards Gender Biased Language Classification: A Case Study with British English Metadata Descriptions

Automatic text summarization systems commonly involve humans for preparing data or evaluating model performance, yet, there lacks a systematic understanding of humans’ roles, experience, and needs when interacting with or being assisted by AI. From a human-centered perspective, we map the design opportunities and considerations for human-AI interaction in text summarization and broader text generation tasks. We first conducted a systematic literature review of 70 papers, developing a taxonomy of five interactions in AI-assisted text generation and relevant design dimensions. We designed text summarization prototypes for each interaction. We then interviewed 16 users, aided by the prototypes, to understand their expectations, experience, and needs regarding efficiency, control, and trust with AI in text summarization and propose design considerations accordingly.

Mapping the Design Space of Human-AI Interaction in Text Summarization

technical paper

https://d3smihljt9218e.cloudfront.net/transcript/36538/subtitle/70dc3374f83ca7279c706069d7b1370a.vtt

English (automatic)

Unifying Parsing and Tree-Structured Models for Generating Sentence Semantic Representations

Downloads

Next from NAACL 2022

Theory-Grounded Measurement of U.S. Social Stereotypes in English Language Models

Similar lecture

MaChAmp at SemEval-2022 Tasks 2, 3, 4, 6, 10, 11, and 12: Multi-task Multi-lingual Learning for a Pre-selected Set of Semantic Datasets