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NAACL 2022

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

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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NAACL 2021

poster

## 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

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

Most research in the area of automatic essay grading (AEG) is geared towards scoring the essay holistically while there has also been little work done on scoring individual essay traits. In this paper, we describe a way to score essays using a multi-task learning (MTL) approach, where scoring the essay holistically is the primary task, and scoring the essay traits is the auxiliary task. We compare our results with a single-task learning (STL) approach, using both LSTMs and BiLSTMs. To find out which traits work best for different types of essays, we conduct ablation tests for each of the essay traits. We also report the runtime and number of training parameters for each system. We find that MTL-based BiLSTM system gives the best results for scoring the essay holistically, as well as performing well on scoring the essay traits. The MTL systems also give a speed-up of between 2.30 to 3.70 times the speed of the STL system, when it comes to scoring the essay and all the traits.

Many Hands Make Light Work: Using Essay Traits to Automatically Score Essays

The goal of our paper is to recognize emotions in conversation.
Many previous studies extract and utilize various knowledge from the speaker's previous utterances.
The knowledge extraction model is pre-trained on an external knowledge dataset.
Therefore, these methods require external structured data.
However, structured data is difficult to access in non-English languages, making it difficult to extend to other languages.
Therefore, we propose a method that does not depend on an external structured dataset.


CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation

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.

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

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