Ireland

ACL 2022

Datasets, Learner Annotation, and Bias in Multiple Languages

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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**Conference Structure**
https://2021.naacl.org/blog/conference-structure/

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NAACL 2021

keynote

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
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, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 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 will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

The Association for Computational Linguistics (ACL) is 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 or natural language processing (NLP). 

When mapping a natural language instruction to a sequence of actions, it is often useful to identify sub-tasks in the instruction. Such sub-task segmentation, however, is not necessarily provided in the training data. We present the A2LCTC (Action-to-Language Connectionist Temporal Classification) algorithm to automatically discover a sub-task segmentation of an action sequence. A2LCTC does not require annotations of correct sub-task segments and learns to find them from pairs of instruction and action sequence in a weakly-supervised manner. We experiment with the ALFRED dataset and show that A2LCTC accurately finds the sub-task structures. With the discovered sub-tasks segments, we also train agents that work on the downstream task and empirically show that our algorithm improves the performance.

Finding Sub-task Structure with Natural Language Instruction

Training a model with access to human explanations can improve data efficiency and model performance on in- and out-of-domain data. Adding to these empirical findings, similarity with the process of human learning makes learning from explanations a promising way to establish a fruitful human-machine interaction. Several methods have been proposed for improving natural language processing (NLP) models with human explanations, that rely on different explanation types and mechanism for integrating these explanations into the learning process. These methods are rarely compared with each other, making it hard for practitioners to choose the best combination of explanation type and integration mechanism for a specific use-case. In this paper, we give an overview of different methods for learning from human explanations, and discuss different factors that can inform the decision of which method to choose for a specific use-case.

A survey on improving NLP models with human explanations

Interpreting NLP models is fundamental for their development as it can shed light on hidden properties and unexpected behaviors. However, while transformer architectures exploit contextual information to enhance their predictive capabilities, most of the available methods to explain such predictions only provide importance scores at the word level. This work addresses the lack of feature attribution approaches that also take into account the sentence structure. We extend the SHAP framework by proposing GrammarSHAP---a model-agnostic explainer leveraging the sentence's constituency parsing to generate hierarchical importance scores.

GrammarSHAP: An Efficient Model-Agnostic and Structure-Aware NLP Explainer

Current QA systems can generate reasonable-sounding yet false answers without explanation or evidence for the generated answer, which is especially problematic when humans cannot readily check the model's answers. This presents a challenge for building trust in machine learning systems. We take inspiration from real-world situations where difficult questions are answered by considering opposing sides (see Irving et al., 2018). For multiple-choice QA examples, we build a dataset of single arguments for both a correct and incorrect answer option in a debate-style set-up as an initial step in training models to produce explanations for two candidate answers. We use long contexts---humans familiar with the context write convincing explanations for pre-selected correct and incorrect answers, and we test if those explanations allow humans who have not read the full context to more accurately determine the correct answer. We do not find that explanations in our set-up improve human accuracy, but a baseline condition shows that providing human-selected text snippets does improve accuracy. We use these findings to suggest ways of improving the debate set up for future data collection efforts.

Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions

Supporting professional fact-checking: how can NLP/AI help?

Model robustness and spurious correlations have received increasing attention in the NLP community, both in methods and evaluation. The term “spurious correlation” has been abused to refer to any undesirable shortcuts learned by the model, judged by domain knowledge. However, in NLP, many features (e.g. word overlap and negation) are not spurious in the sense that the background is a spurious feature to classifying the object in the image. They carry important information that’s needed to make predictions by humans. In this talk, we argue that it is more productive to consider features from the aspects of necessity and sufficiency, and discuss the implications of this categorization in representation, learning, and evaluation.

What We Talk About When We Talk About Spurious Correlations

This presentation will frame the increasing monopolistic power of Big Tech in the field of NLP as an instance of data colonialism. The concept of data colonialism explains the emergence of a new social order based not on the extraction of natural resources or labor, but on the appropriation of human life —including language— through data. This new type of colonialism is already resulting in a highly unequal social arrangement that is deeply incompatible with human freedom and autonomy. And as research in NLP becomes the exclusive domain of powerful corporations, language itself is becoming a zone of colonial extractivism. Resisting this order will require strategies that decolonial thinking has foregrounded for centuries.

Data Colonialism and Language Technology

Multiple studies have shown that Transformers are remarkably robust, and retain performance advantages under massive pruning and other disruptions. Contrary to this received wisdom, in this talk, we will show that pre-trained Transformer encoders are surprisingly fragile to the removal of a very small number of features in the layer outputs. We will also consider the performance of several pre-trained Transformer families and pre-training objectives on a number of downstream probing tasks, and discuss some important challenges to these architectures.

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