Singapore

NLP models often rely on superficial cues known as dataset biases to achieve impressive performance, and can fail on examples where these biases do not hold. Recent work sought to develop robust, unbiased models by filtering biased examples from training sets. In this work, we argue that such filtering can obscure the true capabilities of models to overcome biases, which might never be removed in full from the dataset. We suggest that in order to drive the development of models robust to subtle biases, dataset biases should be amplified in the training set. We introduce an evaluation framework defined by a bias-amplified training set and an anti-biased test set, both automatically extracted from existing datasets. Experiments across four datasets and three models show that our framework is substantially more challenging for models than the original data splits in both fine-tuning and few-shot settings. We hope our work will guide the development of robust models that do not rely on superficial biases and correlations.

EMNLP 2023

Fighting Bias with Bias: Promoting Model Robustness by Amplifying Dataset Biases

minority examples

deep clustering

evaluation

robustness

dataset biases

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
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Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

ACL 2023

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.

workshop paper

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

To access the **EMNLP 2023** event page on Underline, you need to register for the Conference. 
Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

Compositional generalisation is the ability to compose novel combinations from known parts. Recent works have investigated whether neural models are able to achieve such generalisation e.g. handle novel syntactic combinations of known semantic phrases. We apply this idea to morphological composition – the ability to compose the meaning of a word from its constituent morphemes. Our experimental setting is the task of machine translation from English to the Nguni languages, a related group of South African languages that are highly agglutinative - making them ideal for testing morphological modelling. We propose an algorithm for constructing train/test splits with similar morpheme distributions, but diverging word distributions. This allows us to evaluate whether models can handle novel combinations of known morphemes (previously unseen words consisting of previously seen morphemes). We compare results of neural machine translation models using different subword segmenters. All models see performance drops on more challenging evaluation sets, showing that current approaches do not reliably learn morphological composition. Methods that optimise subword segmentation for translation performance (DPE and SSMT) exhibit better performance than standard subword methods (BPE and ULM).

Subword Segmental Machine Translation: Unifying Segmentation and Target Sentence Generation

The language of prompting: What linguistic properties make a prompt successful?

USB: A Unified Summarization Benchmark Across Tasks and Domains

Figures of speech such as metaphors, similes, and idioms are integral parts of human communication. They are ubiquitous in many forms of discourse, allowing people to convey complex, abstract ideas and evoke emotion. As figurative forms are often conveyed through multiple modalities (e.g., both text and images), understanding multimodal figurative language is an important AI challenge, weaving together profound vision, language, commonsense and cultural knowledge.
In this work, we develop the Image Recognition of Figurative Language (IRFL) dataset. We leverage human annotation and an automatic pipeline we created to generate a multimodal dataset, and introduce two novel tasks as a benchmark for multimodal figurative language understanding. We experimented with state-of-the-art vision and language models and found that the best (22%) performed substantially worse than humans (97%). We release our dataset, benchmark, and code, in hopes of driving the development of models that can better understand figurative language.

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Next from EMNLP 2023

Subword Segmental Machine Translation: Unifying Segmentation and Target Sentence Generation

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Fighting Bias With Bias: Promoting Model Robustness by Amplifying Dataset Biases