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Subword tokenization is a commonly used input pre-processing step in most recent NLP models. However, it limits the models&#39; 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&#39; 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.

ACL 2022

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

tokenizer

representation learning

multilingual

**Welcome to ACL-IJCNLP 2021!**

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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).

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.

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. 

 
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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). 

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.

Isomorphic Cross-lingual Embeddings for Low-Resource Languages

We propose the Video Language Co-Attention Network (VLCN) – a novel memory-enhanced model for Video Question Answering (VideoQA). Our model combines two original contributions: A multi-modal fast-learning feature fusion (FLF) block and a mechanism that uses self-attended language features to separately guide neural attention on both static and dynamic visual features extracted from individual video frames and short video clips.
When trained from scratch, VLCN achieves competitive results with the state of the art on both MSVD-QA and MSRVTT-QA with 38.06% and 36.01% test accuracies, respectively.

Video Language Co-Attention with Multimodal Fast-Learning Feature Fusion for VideoQA

State-of-the-art machine learning models are prone to adversarial attacks: Maliciously crafted inputs to fool the model into making a wrong prediction, often with high confidence. While defense strategies have been extensively explored in the computer vision domain, re- search in natural language processing still lacks techniques to make models resilient to adver- sarial text inputs. We adapt a technique from computer vision to detect word-level attacks targeting text classifiers. This method relies on training an adversarial detector leveraging Shapley additive explanations and outperforms the current state-of-the-art on two benchmarks. Furthermore, we prove the detector requires only a low amount of training samples and, in some cases, generalizes to different datasets without needing to retrain.

Detecting Word-Level Adversarial Text Attacks via SHapley Additive exPlanations

Word Mover's Distance is a textual distance metric which calculates the minimum transport cost between two sets of word embeddings. This metric achieves impressive results on semantic similarity tasks, but is slow and difficult to scale due to the large number of floating point calculations. This paper demonstrates that by combining pre-existing lower bounds with binary encoded word vectors, the metric can be rendered highly efficient in terms of computation time and memory while still maintaining accuracy on several textual similarity tasks.

Binary Encoded Word Mover's Distance

This is a short presentation of the "Unsupervised Geometric and Topological Approaches for Cross-Lingual Sentence Representation and Comparison" paper by Shaked Meirom and Omer Bobrowski. This work proposes novel structural-based approaches for the generation and comparison of cross lingual sentence representations. The proposed approaches are fully unsupervised, and rely on geometric and topological methods which are isometric invariant. The quality of the representations, and their preservation across languages, are evaluated in similarity comparison tasks, achieving competitive results. Furthermore, the structural-based representations can be combined with existing methods for improved results.

Unsupervised Geometric and Topological Approaches for Cross-Lingual Sentence Representation and Comparison

Entity linking disambiguates mentions by mapping them to entities in a knowledge graph (KG). One important question in today’s research is how to extend neural entity linking systems to new domains. In this paper, we aim at a system that enables linking mentions to entities from a general-domain KG and a domain-specific KG at the same time. In particular, we represent the entities of different KGs in a joint vector space and address the questions of which data is best suited for creating and fine-tuning that space, and whether fine-tuning harms performance on the general domain. We find that a combination of data from both the general and the special domain is most helpful. The first is especially necessary for avoiding performance loss on the general domain. While additional supervision on entities that appear in both KGs performs best in an intrinsic evaluation of the vector space, it has less impact on the downstream task of entity linking.

A Study on Entity Linking Across Domains":" Which Data is Best for Fine-Tuning?

It's better to say "I can't answer" than to answer incorrectly. This selective prediction ability is crucial for NLP systems to be reliably deployed in real-world applications. Prior work has shown that existing selective prediction techniques fail to perform well, especially in the out-of-domain setting. In this work, we propose a method that improves probability estimates of models by calibrating them using prediction confidence and difficulty score of instances. Using these two signals, we first annotate held-out instances and then train a calibrator to predict the likelihood of correctness of the model's prediction. We instantiate our method with Natural Language Inference (NLI) and Duplicate Detection (DD) tasks and evaluate it in both In-Domain (IID) and Out-of-Domain (OOD) settings. In (IID, OOD) settings, we show that the representations learned by our calibrator result in an improvement of (15.81%, 5.64%) and (6.19%, 13.9%) over 'MaxProb' -- a selective prediction baseline -- on NLI and DD tasks respectively.

Towards Improving Selective Prediction Ability of NLP Systems

Continuous generative models proved their usefulness in high-dimensional data, such as image and audio generation. 
However, continuous models for text generation have received limited attention from the community. In this work, we study 
continuous text generation using Transformers for neural machine translation (NMT). We argue that the choice of embeddings is crucial for such models, so we aim to focus on one particular aspect: target representation via embeddings.
We explore pretrained embeddings and also introduce knowledge transfer from the discrete Transformer model using embeddings in Euclidean and non-Euclidean spaces. Our results on the WMT Romanian-English and English-Turkish benchmarks show such transfer leads to the best-performing continuous model.

On Target Representation in Continuous-output Neural Machine Translation

Pretrained multilingual encoders enable zero-shot cross-lingual transfer, but often produce unreliable models that exhibit high performance variance on the target language. We postulate that this high variance results from zero-shot cross-lingual transfer solving an under-specified optimization problem. We show that any linear-interpolated model between the source language monolingual model and source + target bilingual model has equally low source language generalization error, yet the target language generalization error reduces smoothly and linearly as we move from the monolingual to bilingual model, suggesting that the model struggles to identify good solutions for both source and target languages using the source language alone. Additionally, we show that zero-shot solution lies in non-flat region of target language error generalization surface, causing the high variance.

Zero-shot Cross-lingual Transfer is Under-specified Optimization

Linguistic style is an integral component of language. Recent advances in the development of style representations have increasingly used training objectives from authorship verification (AV): Do two texts have the same author? The assumption underlying the AV training task (same author approximates same writing style) enables self-supervised and, thus, extensive training. However, a good performance on the AV task does not ensure good "general-purpose" style representations. For example, as the same author might typically write about certain topics, representations trained on AV might also encode content information instead of style alone. We introduce a variation of the AV training task that controls for content using conversation or domain labels. We evaluate whether known style dimensions are represented and preferred over content information through an original variation to the recently proposed STEL framework. We find that representations trained by controlling for conversation are better than representations trained with domain or no content control at representing style independent from content.

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Isomorphic Cross-lingual Embeddings for Low-Resource Languages

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