United States

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

**Whova App** 
Stay in touch with your fellow conference attendees via the [Whova App](https://whova.com/portal/webapp/nacon_202106/)

**Conference Structure**
https://2021.naacl.org/blog/conference-structure/

**Walkthrough video of how to NAACL 2021** 

Please take a moment to view this video explaining how to navigate the platform, attend sessions network with other attendees. 


<figure class="video_container">
  <iframe src="https://screencast-o-matic.com/watch/crhwbGVh3vx?v=6&ff=1&title=0&controls=1" width=640  height=350  frameborder="0" allowfullscreen="true"> </iframe>
</figure>

<br>
<br>

NAACL 2021

Saliency methods are widely used to interpret neural network predictions, but different variants of saliency methods often disagree even on the interpretations of the same prediction made by the same model. In these cases, how do we identify when are these interpretations trustworthy enough to be used in analyses? To address this question, we conduct a comprehensive and quantitative evaluation of saliency methods on a fundamental category of NLP models: neural language models. We evaluate the quality of prediction interpretations from two perspectives that each represents a desirable property of these interpretations: plausibility and faithfulness. Our evaluation is conducted on four different datasets constructed from the existing human annotation of syntactic and semantic agreements, on both sentence-level and document-level. Through our evaluation, we identified various ways saliency methods could yield interpretations of low quality. We recommend that future work deploying such methods to neural language models should carefully validate their interpretations before drawing insights.

Evaluating Saliency Methods for Neural Language Models

technical paper

Modeling Human Mental States with an Entity-based Narrative Graph

Self Promotion in US Congressional Tweets

Identifying inherent disagreement in natural language inference

ENTRUST: Argument Reframing with Language Models and Entailment

Hurdles to Progress in Long-form Question Answering

An Empirical Study on Neural Keyphrase Generation

A Simple and Efficient Multi-Task Learning Approach for Conditioned Dialogue Generation

Paragraph-level Simplification of Medical Texts

Attention Head Masking for Inference Time Content Selection in Abstractive Summarization

Factual Probing Is [MASK]: Learning vs. Learning to Recall

https://d3smihljt9218e.cloudfront.net/transcript/13809/subtitle/d44fee342fc78ac79f0739ac95767a4b.vtt

English (automatic)

Evaluating Saliency Methods for Neural Language Models

Downloads

Next from NAACL 2021

Modeling Human Mental States with an Entity-based Narrative Graph

Similar lecture

Adapting Coreference Resolution for Processing Violent Death Narratives