United States

NAACL 2022

Simulating Feature Structures with Simple Types

preprocessor

abstract categorial grammars

feature structures

## 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

Generative dialogue models suffer badly from the generic response problem, limiting
their applications to a few toy scenarios. Recently, an interesting approach, namely
negative training, has been proposed to alleviate this problem by reminding the model
not to gener- ate high-frequency responses during training. However, its performance is hindered by two issues, ignoring low-frequency but generic responses and bringing low-frequency but meaningless responses. In this paper, we propose a novel negative training paradigm, called nega- tive distillation, to keep the model away from the
undesirable generic responses while avoiding the above problems. First, we introduce a negative teacher model that can produce query- wise generic responses, and then
the student model is required to maximize the distance with multi-level negative
knowledge. Em- pirical results show that our method outperforms previous negative
training methods significantly.

Diversifying Neural Dialogue Generation via Negative Distillation

Text Style Transfer for Bias Mitigation using Masked Language Modeling

Differentially Private Instance Encoding against Privacy Attacks

Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation

Multiformer: A Head-Configurable Transformer-Based Model for Direct Speech Translation

Towards Unsupervised Speech Synthesis

Investigating the effectiveness of various speaker embeddings for multi-speaker end-to-end speech synthesis system using small-sized speech data

Eliciting Complex Relational Knowledge From Masked Language Models

Methods for Estimating and Improving Robustness of Language Models

Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation

technical paper

https://d3smihljt9218e.cloudfront.net/transcript/36501/subtitle/0bc218057c319d44eb1f91a4451ccf96.vtt

English (automatic)

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Diversifying Neural Dialogue Generation via Negative Distillation

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