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An explosion in the popularity of transformer-based language models (such as GPT-3, BERT, RoBERTa, and ALBERT) has opened the doors to new machine learning applications involving language modeling, text generation, and more. However, recent scrutiny reveals that these language models contain inherent biases towards certain demographics reflected in their training data. While research has tried mitigating this problem, existing approaches either fail to remove the bias completely, degrade performance (&quot;catastrophic forgetting&quot;), or are costly to execute. This work examines how to reduce gender bias in a GPT-2 language model by fine-tuning less than 1% of its parameters. Through quantitative benchmarks, we show that this is a viable way to reduce prejudice in pre-trained language models while remaining cost-effective at scale.

ACL 2022

Debiasing Pre-Trained Language Models via Efficient Fine-Tuning

debiasing language models

efficient fine-tuning

fairness

bias

fine-tuning

language models

# 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). 

An explosion in the popularity of transformer-based language models (such as GPT-3, BERT, RoBERTa, and ALBERT) has opened the doors to new machine learning applications involving language modeling, text generation, and more. However, recent scrutiny reveals that these language models contain inherent biases towards certain demographics reflected in their training data. While research has tried mitigating this problem, existing approaches either fail to remove the bias completely, degrade performance ("catastrophic forgetting"), or are costly to execute. This work examines how to reduce gender bias in a GPT-2 language model by fine-tuning less than 1% of its parameters. Through quantitative benchmarks, we show that this is a viable way to reduce prejudice in pre-trained language models while remaining cost-effective at scale.

workshop paper


Many datasets contain personally identifiable information, or PII, which poses privacy risks to individuals. PII masking is commonly used to redact personal information such as names, addresses, and phone numbers from text data. Most modern PII masking pipelines involve machine learning algorithms. However, these systems may vary in performance, such that individuals from particular demographic groups bear a higher risk for having their personal information exposed. In this paper, we evaluate the performance of three off-the-shelf PII masking systems on name detection and redaction. We generate data using names and templates from the customer service domain. We find that an open-source RoBERTa-based system shows fewer disparities than the commercial models we test. However, all systems demon- strate significant differences in error rate based on demographics. In particular, the highest error rates occurred for names associated with Black and Asian/Pacific Islander individuals.

Behind the Mask: Demographic bias in name detection for PII masking

As natural language processing systems become more widespread, it is necessary to address fairness issues in their implementation and deployment to ensure that their negative impacts on society are understood and minimized. However, there is limited work that studies fairness using a multilingual and intersectional framework or on downstream tasks. In this paper, we introduce four multilingual Equity Evaluation Corpora, supplementary test sets designed to measure social biases, and a novel statistical framework for studying unisectional and intersectional social biases in natural language processing. We use these tools to measure gender, racial, ethnic, and intersectional social biases across five models trained on emotion regression tasks in English, Spanish, and Arabic. We find that many systems demonstrate statistically significant unisectional and intersectional social biases.

Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic

Natural language processing is a powerful tool that can analyze a person's mental state from text they have written. Models have been developed that can predict whether a person is experiencing a mental health condition from social media posts with high accuracy. Yet, these models cannot explain why the person is experiencing a particular mental state. In this work, we present a new method for explaining a person's mental state from text using Monte Carlo tree search (MCTS). Our MCTS algorithm employs trained classification models to guide the search for key phrases that explain the writer's mental state in a concise, interpretable manner. Furthermore, our algorithm can find both explanations that depend on the particular context of the text (e.g., a recent breakup) and those that are context-independent. Using a dataset of Reddit posts that exhibit stress, we demonstrate the ability of our MCTS algorithm to identify interpretable explanations for a person's feeling of stress in both a context-dependent and context-independent manner.


Monte Carlo Tree Search for Interpreting Stress in Natural Language

This paper presents a state-of-the-art solution to the LT-EDI-ACL 2022 Task 4: \textit{Detecting Signs of Depression from Social Media Text}. The goal of this task is to detect the severity levels of depression of people from social media posts, where people often share their feelings on a daily basis. To detect the signs of depression, we propose a framework with pre-trained language models using rich information instead of training from scratch, gradient boosting and deep learning models for modeling various aspects, and supervised contrastive learning for the generalization ability. Moreover, ensemble techniques are also employed in consideration of the different advantages of each method. Experiments show that our framework achieves a 2nd prize ranking with a macro F1-score of 0.552, showing the effectiveness and robustness of our approach.

Ensemble Models with VADER and Contrastive Learning for Detecting Signs of Depression from Social Media

This working-notes are about the participation of the UMUTeam in a LT-EDI shared task concerning the identification of homophobic and transphobic comments in YouTube. These comments are written in English, which has high availability to machine-learning resources; Tamil, which has fewer resources; and a transliteration from Tamil to Roman script combined with English sentences. To carry out this shared task, we train a neural network that combines several feature sets applying a knowledge integration strategy. These features are linguistic features extracted from a tool developed by our research group and contextual and non-contextual sentence embeddings. We ranked 7th for English subtask (macro f1-score of 45%), 3rd for Tamil subtask (macro f1-score of 82%), and 2nd for Tamil-English subtask (macro f1-score of 58%).

Detecting homophobic and transphobic comments in Tamil

Depression is a mental condition related to sadness and the lack of interest in common daily tasks. In this working-notes, we describe the proposal of the UMUTeam in the LT-EDI shared task (ACL 2022) concerning the identification of signs of depression in social network posts. This task is somehow related to other relevant Natural Language Processing tasks such as Emotion Analysis. In this shared task, the organisers challenged the participants to distinguish between moderate and severe signs of depression (or no signs of depression at all) in a set of social posts written in English. Our proposal is based on the combination of linguistic features and several sentence embeddings using a knowledge integration strategy. Our proposal achieved the 6th position, with a macro f1-score of 53.82 in the official leader board.

Detecting Signs of Depression from text

Online social networks are ubiquitous and user-friendly. Nevertheless, it is vital to detect and moderate offensive content to maintain decency and empathy. However, mining social media texts is a complex task since users don't adhere to any fixed patterns. Comments can be written in any combination of languages and many of them may be low-resource.

In this paper, we present our system for the LT-EDI shared task on detecting homophobia and transphobia in social media comments. We experiment with a number of monolingual and multilingual transformer based models such as mBERT along with a data augmentation technique for tackling class imbalance. Such pretrained large models have recently shown tremendous success on a variety of benchmark tasks in natural language processing. We observe their performance on a carefully annotated, real life dataset of YouTube comments in English as well as Tamil.

Our submission achieved ranks 9, 6 and 3 with a macro-averaged F1-score of 0.42, 0.64 and 0.58 in the English, Tamil and Tamil-English subtasks respectively. The code for the system has been open sourced.

Leveraging Pretrained Language Models for Detecting Homophobia and Transphobia in Social Media Comments

This paper describes our system that participated in LT-EDI-ACL2022- Homophobia/Transphobia Detection in Social Media. Sexual minorities face a lot of unfair treatment and discrimination in our world. This creates enormous stress and many psychological problems for sexual minorities. There is a lot of hate speech on the internet, and homophobia/transphobia is one against sexual minorities. Identifying and processing homophobia/ transphobia through natural language processing technology can improve the efficiency of processing it, and can quickly screen out it on the Internet. The organizer of LT-EDI-ACL2022- Homophobia/Transphobia Detection in Social Media constructs a homophobia/ transphobia detection dataset based on YouTube comments for English and Tamil. We use a Roberta -based approach to conduct homophobia/transphobia detection experiments on the dataset of the competition, and get better results.

A Roberta based Approach for Homophobia/Transphobia Detection in Social Media

Spreading positive vibes or hope content on social media may help many people to get motivated in their life. To address Hope Speech detection in YouTube comments, this paper presents the description of the models submitted by our team - MUCIC, to the Hope Speech Detection for Equality, Diversity, and Inclusion (HopeEDI) shared task at Association for Computational Linguistics (ACL) 2022. This shared task consists of texts in five languages, namely: English, Spanish (in Latin scripts), and Tamil, Malayalam, and Kannada (in code-mixed native and Roman scripts) with the aim of classifying the YouTube comment into "Hope", "Not-Hope" or "Not-Intended" categories. The proposed methodology uses the re-sampling technique to deal with imbalanced data in the corpus and obtained 1st rank for English language with a macro-averaged F1-score of 0.550 and weighted-averaged F1-score of 0.860. The code to reproduce this work is available in https://github.com/anushamdgowda/Hope-speech.

Hope Speech Detection using Data Re-Sampling and 1D Conv-LSTM

In recent years social media has become one of the major forums for expressing human views and emotions. With the help of smartphones and high-speed internet, anyone can express their views on Social media. However, this can also lead to the spread of hatred and violence in society. Therefore it is necessary to build a method to find and support helpful social media content. In this paper, we studied Natural Language Processing approach for detecting Hope speech in a given sentence. The task was to classify the sentences into ‘Hope speech’ and ‘Non-hope speech’. The dataset was provided by LT-EDI organizers with text from Youtube comments. Based on the task description, we developed a system using the pre-trained language model BERT to complete this task. Our model achieved 1st rank in the Kannada language with a weighted average F1 score of 0.750, 2nd rank in the Malayalam language with a weighted average F1 score of 0.740, 3rd rank in the Tamil language with a weighted average F1 score of 0.390 and 6th rank in the English language with a weighted average F1 score of 0.880.

Debiasing Pre-Trained Language Models via Efficient Fine-Tuning

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Behind the Mask: Demographic bias in name detection for PII masking

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Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic

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