Ireland

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.

ACL 2022

Data Colonialism and Language Technology

# 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). 

keynote

Distributional semantic models capture word-level meaning that is useful in many natural language processing tasks and have even been shown to capture cognitive aspects of word meaning. The majority of these models are purely text based, even though the human sensory experience is much richer. In this paper we create visually grounded word embeddings by combining English text and images and compare them to popular text-based methods, to see if visual information allows our model to better capture cognitive aspects of word meaning. Our analysis shows that visually grounded embedding similarities are more predictive of the human reaction times in a large priming experiment than the purely text-based embeddings. The visually grounded embeddings also correlate well with human word similarity ratings. Importantly, in both experiments we show that the grounded embeddings account for a unique portion of explained variance, even when we include text-based embeddings trained on huge corpora. This shows that visual grounding allows our model to capture information that cannot be extracted using text as the only source of information.

Seeing the advantage: visually grounding word embeddings to better capture human semantic knowledge

We propose a new neural model for word embeddings, which uses Unitary 
Matrices as the primary device for encoding lexical information. It uses
simple matrix multiplication to derive matrices for large units, yielding
a sentence processing model that is strictly compositional, does not
lose information over time steps, and is transparent, in the sense
that word embeddings can be analysed regardless of context.
This model does not employ activation functions, and so
the network is fully accessible to analysis by the methods of linear
algebra at each point in its operation on an input sequence. We test
it in two NLP agreement tasks and obtain rule like perfect accuracy,
with greater stability than current state-of-the-art systems. Our proposed
model goes some way towards offering a class of computationally powerful 
deep learning systems that can be fully understood and compared 
to human cognitive processes for natural language learning and
representation.


A Neural Model for Compositional Word Embeddings and Sentence Processing

In our study, we create Codenames spymaster agents using four types of relatedness measures that require only a raw text corpus to produce. These include newly introduced ones based on co-occurrences, which outperform FastText cosine similarity on gold standard relatedness data. To generate clues in Codenames, we combine relatedness measures with four different scoring functions, for two languages, English and Hungarian. For testing, we collect decisions of human guesser players in an online game, and our configurations outperform previous agents among methods using raw corpora only.

Codenames as a Game of Co-occurrence Counting

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

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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