Ireland

Past works that investigate out-of-domain performance of QA systems have mainly focused on general domains (e.g. news domain, wikipedia domain), underestimating the importance of subdomains defined by the internal characteristics of QA datasets. In this paper, we extend the scope of &quot;out-of-domain&quot; by splitting QA examples into different subdomains according to their several internal characteristics including question type, text length, answer position. We then examine the performance of QA systems trained on the data from different subdomains. Experimental results show that the performance of QA systems can be significantly reduced when the train data and test data come from different subdomains. These results question the generalizability of current QA systems in multiple subdomains, suggesting the need to combat the bias introduced by the internal characteristics of QA datasets.

ACL 2022

Extending the Scope of Out-of-Domain: Examining QA models in multiple subdomains

answer position

text length

question type

internal characteristics

out-of-subdomain generalizability

question answsering

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

Past works that investigate out-of-domain performance of QA systems have mainly focused on general domains (e.g. news domain, wikipedia domain), underestimating the importance of subdomains defined by the internal characteristics of QA datasets. In this paper, we extend the scope of "out-of-domain" by splitting QA examples into different subdomains according to their several internal characteristics including question type, text length, answer position. We then examine the performance of QA systems trained on the data from different subdomains. Experimental results show that the performance of QA systems can be significantly reduced when the train data and test data come from different subdomains. These results question the generalizability of current QA systems in multiple subdomains, suggesting the need to combat the bias introduced by the internal characteristics of QA datasets.

workshop paper

# 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) 
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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). 

Neural machine learning models can successfully model language that is similar to their training distribution, but they are highly susceptible to degradation under distribution shift, which occurs in many practical applications when processing out-of-domain (OOD) text. This has been attributed to "shortcut learning": relying on weak correlations over arbitrary large contexts. 

We propose a method based on OOD detection with Random Network Distillation to allow an autoregressive language model to automatically disregard OOD context during inference, smoothly transitioning towards a less expressive but more robust model as the data becomes more OOD, while retaining its full context capability when operating in-distribution. 

We apply our method to a GRU architecture, demonstrating improvements on
multiple language modeling (LM) datasets.

Distributionally Robust Recurrent Decoders with Random Network Distillation

Simultaneous training of a multi-task learning (MTL) network on different domains or tasks is not always straightforward. It could lead to inferior performance or generalization compared to the corresponding single-task networks. An effective training scheduling method is deemed necessary to maximize the benefits of multitask learning. Traditional schedulers follow a heuristic or prefixed strategy, ignoring the relation of the tasks, their sample complexities, and the state of the emergent shared features. We proposed a deep Q-Learning Scheduler (QLS) that monitors the state of the tasks and the shared features using a novel histogram of task uncertainty, and through trial-and-error, learns an optimal policy for task scheduling. Extensive experiments on multi-domain and multitask settings with various task difficulty profiles have been conducted, the proposed method is benchmarked against other schedulers, its superior performance has been demonstrated, and results are discussed.

Q-Learning Scheduler for Multi Task Learning Through the use of Histogram of Task Uncertainty

CLIP, a vision-language network trained with a multimodal contrastive learning objective on a large dataset of images and captions, has demonstrated impressive zero-shot ability in various tasks. However, recent work showed that in comparison to unimodal (visual) networks, CLIP's multimodal training does not benefit generalization (e.g. few-shot or transfer learning) for standard visual classification tasks such as object, street numbers or animal recognition. Here, we hypothesize that CLIP's improved unimodal generalization abilities may be most prominent in domains that involve human-centric concepts (cultural, social, aesthetic, affective...); this is because CLIP's training dataset is mainly composed of image annotations made by humans for other humans. To evaluate this, we use 3 tasks that require judging human-centric concepts: sentiment analysis on tweets, genre classification on books or movies. We introduce and publicly release a new multimodal dataset for movie genre classification. We compare CLIP's visual stream against two visually trained networks and CLIP's textual stream against two linguistically trained networks, as well as multimodal combinations of these networks. We show that CLIP generally outperforms other networks, whether using one or two modalities. We conclude that CLIP's multimodal training is beneficial for both unimodal and multimodal tasks that require classification of human-centric concepts.

When does CLIP generalize better than unimodal models? When judging human-centric concepts

We choose random points in the hyperbolic disc and claim that these points are already word representations. However, it is yet to be uncovered which point corresponds to which word of the human language of interest. This correspondence can be approximately established using a pointwise mutual information between words and recent alignment techniques.

From Hyperbolic Geometry Back to Word Embeddings

Transformers have shown to be very powerful models for named entity recognition (NER). However, in low-resource scenarios, can pre-trained encoders from transformer-based languege models provide useful information for this task? In this talk, we present our encoder evaluation framework for few-shot NER and share some interesting results of a wide range of encoders on 10 benchmark datasets. The findings will hopefully help us with the choice of model for (few-shot) NER.

A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition

Task-adaptive pre-training (TAPT) alleviates the lack of labelled data and provides performance lift by adapting unlabelled data to downstream task. Unfortunately, existing adaptations mainly involve deterministic rules that cannot generalize well. Here, we propose Clozer, a sequence-tagging based cloze answer extraction method used in TAPT that is extendable for adaptation on any cloze-style machine reading comprehension (MRC) downstream tasks. We experiment on multiple-choice cloze-style MRC tasks, and show that Clozer performs significantly better compared to the oracle and state-of-the-art in escalating TAPT effectiveness in lifting model performance, and prove that Clozer is able to recognize the gold answers independently of any heuristics.

Clozer: Adaptable Data Augmentation for Cloze-style Reading Comprehension

Multilingual language models were shown to allow for nontrivial transfer across scripts and languages. In this work, we study the structure of the internal representations that enable this transfer. We focus on the representation of gender distinctions as a practical case study, and examine the extent to which the gender concept is encoded in shared subspaces across different languages. Our analysis shows that gender representations consist of several prominent components that are shared across languages, alongside language-specific components. The existence of language-independent and language-specific components provides an explanation for an intriguing empirical observation we make: while gender classification transfers well across languages, interventions for gender removal, trained on a single language, do not transfer easily to others.

Analyzing Gender Representation in Multilingual Models

Although deep neural networks have achieved state-of-the-art performance in various machine learning tasks, adversarial examples, constructed by adding small non-random perturbations to correctly classified inputs, successfully fool highly expressive deep classifiers into incorrect predictions. Approaches to adversarial attacks in natural language tasks have boomed in the last five years using character-level, word-level, phrase-level, or sentence-level textual perturbations. While there is some work in NLP on defending against such attacks through proactive methods, like adversarial training, there is to our knowledge no effective general reactive approaches to defence via detection of textual adversarial examples such as is found in the image processing literature. In this paper, we propose two new reactive methods for NLP to fill this gap, which unlike the few limited application baselines from NLP are based entirely on distribution characteristics of learned representations: we adapt one from the image processing literature (Local Intrinsic Dimensionality (LID)), and propose a novel one (MultiDistance Representation Ensemble Method (MDRE)). Adapted LID and MDRE obtain state-of-the-art results on character-level, word-level, and phrase-level attacks on the IMDB dataset as well as on the later two with respect to the MultiNLI dataset. For future research, we publish our code \footnote{Code available at \url{https://github.com/NaLiuAnna/MDRE}}.

Detecting Textual Adversarial Examples Based on Distributional Characteristics of Data Representations

Subword tokenization is a commonly used input pre-processing step in most recent NLP models. However, it limits the models' ability to leverage end-to-end task learning. Its frequency-based vocabulary creation compromises tokenization in low-resource languages, leading models to produce suboptimal representations. Additionally, the dependency on a fixed vocabulary limits the subword models' adaptability across languages and domains. In this work, we propose a vocabulary-free neural tokenizer by distilling segmentation information from heuristic-based subword tokenization. We pre-train our character-based tokenizer by processing unique words from multilingual corpus, thereby extensively increasing word diversity across languages. Unlike the predefined and fixed vocabularies in subword methods, our tokenizer allows end-to-end task learning, resulting in optimal task-specific tokenization. The experimental results show that replacing the subword tokenizer with our neural tokenizer consistently improves performance on multilingual (NLI) and code-switching (sentiment analysis) tasks, with larger gains in low-resource languages. Additionally, our neural tokenizer exhibits a robust performance on downstream tasks when adversarial noise is present (typos and misspelling), further increasing the initial improvements over statistical subword tokenizers.

A Vocabulary-Free Multilingual Neural Tokenizer for End-to-End Task Learning

Cross-Lingual Word Embeddings (CLWEs) are a key component to transfer linguistic information learnt from higher-resource settings into lower-resource ones. Recent research in cross-lingual representation learning has focused on offline mapping approaches due to their simplicity, computational efficacy, and ability to work with minimal parallel resources. However, they crucially depend on the assumption of embedding spaces being approximately isomorphic i.e. sharing similar geometric structure, which does not hold in practice, leading to poorer performance on low-resource and distant language pairs. In this paper, we introduce a framework to learn CLWEs, without assuming isometry, for low-resource pairs via joint exploitation of a related higher-resource language. In our work, we first pre-align the low-resource and related language embedding spaces using offline methods to mitigate the assumption of isometry. Following this, we use joint training methods to develops CLWEs for the related language and the target embedding space. Finally, we remap the pre-aligned low-resource space and the target space to generate the final CLWEs. We show consistent gains over current methods in both quality and degree of isomorphism, as measured by bilingual lexicon induction (BLI) and eigenvalue similarity respectively, across several language pairs: {Nepali, Finnish, Romanian, Gujarati, Hungarian}-English. Lastly, our analysis also points to the relatedness as well as the amount of related language data available as being key factors in determining the quality of embeddings achieved.

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Distributionally Robust Recurrent Decoders with Random Network Distillation

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