Taiwan

Recent methods based on pre-trained language models have exhibited superior performance over tabular tasks (e.g., tabular NLI), despite showing inherent problems such as not using the right evidence and inconsistent predictions across inputs while reasoning over the tabular data (Gupta et al., 2021). In this work, we utilize Pattern-Exploiting Training (PET) (i.e., strategic MLM) on pre-trained language models to strengthen these tabular reasoning models&#39; pre-existing knowledge and reasoning abilities. Our upgraded model exhibits a superior understanding of knowledge facts and tabular reasoning compared to current baselines. Additionally, we demonstrate that such models are more effective for underlying downstream tasks of tabular inference on INFOTABS. Furthermore, we show our model&#39;s robustness against adversarial sets generated through various character and word level perturbations.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.54/)

IJCNLP-AACL 2022

Enhancing Tabular Reasoning with Pattern Exploiting Training

knowledge-based reasoning

infotabs

tabular reasoning

## 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/)

NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Recent methods based on pre-trained language models have exhibited superior performance over tabular tasks (e.g., tabular NLI), despite showing inherent problems such as not using the right evidence and inconsistent predictions across inputs while reasoning over the tabular data (Gupta et al., 2021). In this work, we utilize Pattern-Exploiting Training (PET) (i.e., strategic MLM) on pre-trained language models to strengthen these tabular reasoning models' pre-existing knowledge and reasoning abilities. Our upgraded model exhibits a superior understanding of knowledge facts and tabular reasoning compared to current baselines. Additionally, we demonstrate that such models are more effective for underlying downstream tasks of tabular inference on INFOTABS. Furthermore, we show our model's robustness against adversarial sets generated through various character and word level perturbations.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.54/)

poster

The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP).

A registration is required to access this event. Access to the event site will be granted to all registered attendees on **November 16th**. If you have not already done so, please register using the link below:
 
 https://conference.iis.sinica.edu.tw/servlet/Register?ConferenceID=411

Registration Is Required

The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP). 

Existing self-supervised learning strategies are constrained to either a limited set of objectives or generic downstream tasks that predominantly target uni-modal applications. This has isolated progress for imperative multimodal applications that are diverse in terms of complexity and domain-affinity, such as meme analysis. Here, we introduce two self-supervised pre-training methods, namely Ext-PIE-Net and MM-SimCLR that (i) employ off-the-shelf multi-modal hate-speech data during pre-training and (ii) perform selfsupervised learning by incorporating multiple specialized pretext tasks, effectively catering to the required complex multi-modal representation learning for meme analysis. 
We experiment with different self-supervision strategies, including potential variants that could help learn rich cross-modality representations and evaluate using popular linear probing on the Hateful Memes task. The proposed solutions strongly compete with the fully supervised baseline via label-efficient training while distinctly outperforming them on all three tasks of the Memotion challenge with 0.18%, 23.64%, and 0.93% performance gain, respectively. Further, we demonstrate the generalizability of the proposed solutions by reporting competitive performance on the HarMeme task. Finally, we empirically establish the quality of the learned representations by analyzing task-specific learning, using fewer labeled training samples, and arguing that the complexity of the self-supervision strategy and downstream task at hand are correlated. Our efforts highlight the requirement of better multi-modal self-supervision methods involving specialized pretext tasks for efficient fine-tuning and generalizable performance.

Domain-aware Self-supervised Pre-training for Label-Efficient Meme Analysis

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.10/)

Missing Modality meets Meta Sampling (M3S): An Efficient Universal Approach for Multimodal Sentiment Analysis with Missing Modality

Emotion recognition in conversation (ERC) has attracted much attention in recent years for its necessity in widespread applications. With the development of graph neural network (GNN), recent state-of-the-art ERC models mostly use GNN to embed the intrinsic structure informa- tion of a conversation into the utterance fea- tures. In this paper, we propose a novel GNN- based model for ERC, namely S+PAGE, to bet- ter capture the speaker and position-aware con- versation structure information. Specifically, we add the relative positional encoding and speaker dependency encoding in the represen- tations of edge weights and edge types respec- tively to acquire a more reasonable aggregation algorithm for ERC. Besides, a two-stream con- versational Transformer is presented to extract both the self and inter-speaker contextual fea- tures for each utterance. Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison, whose results demonstrate the superiority of our model.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.12/)

S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation

Knowledge graphs are increasingly used in a plethora of downstream tasks or in the augmentation of statistical models to improve factuality. However, social biases are engraved in these representations and propagate downstream. We conducted a critical analysis of literature concerning biases at different steps of a knowledge graph lifecycle. We investigated factors introducing bias, as well as the biases that are rendered by knowledge graphs and their embedded versions afterward. Limitations of existing measurement and mitigation strategies are discussed and paths forward are proposed.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.49/)

The Lifecycle of "Facts": On Social Biases in Knowledge Graphs

Recent research has revealed undesirable biases in NLP data and models. However, these efforts focus of social disparities in West, and are not directly portable to other geo-cultural contexts. In this paper, we focus on NLP fairness in the context of India. We start with a brief account of the prominent axes of social disparities in India. We build resources for fairness evaluation in the Indian context and use them to demonstrate prediction biases along some of the axes. We then delve deeper into social stereotypes for Region and Religion, demonstrating its prevalence in corpora and models. Finally, we outline a holistic research agenda to re-contextualize NLP fairness research for the Indian context, accounting for Indian societal context, bridging technological gaps in NLP capabilities and resources, and adapting to Indian cultural values. While we focus on India, this framework can be generalized to other geo-cultural contexts.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.55/)

Re-contextualizing Fairness in NLP: The Case of India

VScript is a controllable script generation system including dialogues and scene descriptions, as well as presents visually using video retrieval. We adopt a hierarchical structure for our framework by implementing high-level planning and following the guidelines of video-making processes.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-demo.1/)

VScript: Controllable Script Generation with Visual Presentation

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-demo.2/)


TexPrax: A Messaging Application for Ethical, Real-time Data Collection and Annotation

Children with language disabilities face communication difficulties in daily life. They are often deprived of the opportunity to participate in social activities due to their difficulty in understanding or using natural language. In this regard, Augmentative and Alternative Communication (AAC) can be a practical means of communication for children with language disabilities. In this study, we propose PicTalky, which is an AI-based AAC system that helps children with language developmental disabilities to improve their communication skills and language comprehension abilities. PicTalky can process both text and pictograms more accurately by connecting a series of neural-based NLP modules. Additionally, we perform quantitative and qualitative analyses on the modules of PicTalky. By using this service, it is expected that those suffering from language problems will be able to express their intentions or desires more easily and improve their quality of life. We have made the models freely available alongside a demonstration of the web interface. Furthermore, we implemented robotics AAC for the first time by applying PicTalky to the NAO robot.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-demo.3/)


PicTalky: Augmentative and Alternative Communication for Language Developmental Disabilities

Question Answering (QA) systems are increasingly deployed in applications where they support real-world decisions. However, state-of-the-art models rely on deep neural networks, which are difficult to interpret by humans. Inherently interpretable models or post hoc explainability methods can help users to comprehend how a model arrives at its prediction and, if successful, increase their trust in the system. Furthermore, researchers can leverage these insights to develop new methods that are more accurate and less biased. In this paper, we introduce SQuARE v2, the new version of SQuARE, to provide an explainability infrastructure for comparing models based on methods such as saliency maps and graph-based explanations. While saliency maps are useful to inspect the importance of each input token for the model's prediction, graph-based explanations from external Knowledge Graphs enable the users to verify the reasoning behind the model prediction. In addition, we provide multiple adversarial attacks to compare the robustness of QA models. With these explainability methods and adversarial attacks, we aim to ease the research on trustworthy QA models. SQuARE is available at https://square.ukp-lab.de.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-demo.4/)


UKP-SQuARE v2: Explainability and Adversarial Attacks for Trustworthy QA

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-demo.5/)


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Domain-aware Self-supervised Pre-training for Label-Efficient Meme Analysis

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Enhancing Tabular Reasoning with Pattern Exploiting Training