United Arab Emirates

Bilingual lexicon induction (BLI) with limited bilingual supervision is a crucial yet challenging task in multilingual NLP. Current state-of-the-art BLI methods rely on the induction of cross-lingual word embeddings (CLWEs) to capture cross-lingual word similarities; such CLWEs are obtained 1) via traditional static models (e.g., VecMap), or 2) by extracting type-level CLWEs from multilingual pretrained language models (mPLMs), or 3) through combining the former two options. In this work, we propose a novel semi-supervised post-hoc reranking method termed BLICEr (BLI with Cross-Encoder Reranking), applicable to any precalculated CLWE space, which improves their BLI capability. The key idea is to &#39;extract&#39; cross-lingual lexical knowledge from mPLMs, and then combine it with the original CLWEs. This crucial step is done via 1) creating a word similarity dataset, comprising positive word pairs (i.e., true translations) and hard negative pairs induced from the original CLWE space, and then 2) fine-tuning an mPLM (e.g., mBERT or XLM-R) in a cross-encoder manner to predict the similarity scores. At inference, we 3) combine the similarity score from the original CLWE space with the score from the BLI-tuned cross-encoder. BLICEr establishes new state-of-the-art results on two standard BLI benchmarks spanning a wide spectrum of diverse languages: it substantially outperforms a series of strong baselines across the board. We also validate the robustness of BLICEr with different CLWEs. Our code is available at https://github.com/cambridgeltl/BLICEr.

EMNLP 2022

Improving Bilingual Lexicon Induction with Cross-Encoder Reranking

bilingual lexicon induction

word translation

cross-encoder

Bilingual lexicon induction (BLI) with limited bilingual supervision is a crucial yet challenging task in multilingual NLP. Current state-of-the-art BLI methods rely on the induction of cross-lingual word embeddings (CLWEs) to capture cross-lingual word similarities; such CLWEs are obtained 1) via traditional static models (e.g., VecMap), or 2) by extracting type-level CLWEs from multilingual pretrained language models (mPLMs), or 3) through combining the former two options. In this work, we propose a novel semi-supervised post-hoc reranking method termed BLICEr (BLI with Cross-Encoder Reranking), applicable to any precalculated CLWE space, which improves their BLI capability. The key idea is to 'extract' cross-lingual lexical knowledge from mPLMs, and then combine it with the original CLWEs. This crucial step is done via 1) creating a word similarity dataset, comprising positive word pairs (i.e., true translations) and hard negative pairs induced from the original CLWE space, and then 2) fine-tuning an mPLM (e.g., mBERT or XLM-R) in a cross-encoder manner to predict the similarity scores. At inference, we 3) combine the similarity score from the original CLWE space with the score from the BLI-tuned cross-encoder. BLICEr establishes new state-of-the-art results on two standard BLI benchmarks spanning a wide spectrum of diverse languages: it substantially outperforms a series of strong baselines across the board. We also validate the robustness of BLICEr with different CLWEs. Our code is available at https://github.com/cambridgeltl/BLICEr.

workshop paper

## Welcome to EMNLP 2022!
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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

In an effort to study the inductive biases of language models, numerous studies have attempted to use linguistically motivated tasks as a proxy of sorts, wherein performance on these tasks would imply an inductive bias towards a specific linguistic phenomenon. In this study, we attempt to analyse the inductive biases of language models with respect to natural language phenomena
in the context of building multilingual embedding spaces.
We sample corpora from 2 sources in 15 languages and train language models on pseudo-bilingual variants of each corpus, created by duplicating each corpus and shifting token indices for half the resulting corpus. We evaluate the cross-lingual capabilities of these LMs, and show that while correlations with language families tend to be weak, other corpus-level characteristics, such as type-token ratio, tend to be more strongly correlated. Finally, we show that multilingual spaces can be built, albeit less effectively, even when additional destructive perturbations are applied to the training corpora, implying that (effectively) bag-of-words models also have an inductive bias that is sufficient for inducing multilingual spaces.

The Effects of Corpus Choice and Morphosyntax on Multilingual Space Induction

Most vision-and-language pretraining research focuses on English tasks. However, the creation of multilingual multimodal evaluation datasets (e.g. Multi30K, xGQA, XVNLI, and MaRVL) poses a new challenge in finding high-quality training data that is both multilingual and multimodal. We investigate whether machine translating English multimodal data can be an effective proxy for the lack of readily available multilingual data. We call this framework TD-MML: Translated Data for Multilingual Multimodal Learning, and it can be applied to any multimodal dataset and model. We apply it to both pretraining and fine-tuning data with a state-of-the-art model. In order to prevent models from learning from low-quality translated text, we propose two metrics for automatically removing such translations from the resulting datasets. In experiments on five tasks across 20 languages in the IGLUE benchmark, we show that translated data can provide a useful signal for multilingual multimodal learning, both at pretraining and fine-tuning.

Multilingual Multimodal Learning with Machine Translated Text

Timely and effective response to humanitarian crises requires quick and accurate analysis of large amounts of text data - a process that can highly benefit from expert-assisted NLP systems trained on validated and annotated data in the humanitarian response domain. To enable creation of such NLP systems, we introduce and release HumSet, a novel and rich multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response community. The dataset provides documents in three languages (English, French, Spanish) and covers a variety of humanitarian crises from 2018 to 2021 across the globe. For each document, HumSet provides selected snippets (entries) as well as assigned classes to each entry annotated using common humanitarian information analysis frameworks. HumSet also provides novel and challenging entry extraction and multi-label entry classification tasks. In this paper, we take a first step towards approaching these tasks and conduct a set of experiments on Pre-trained Language Models (PLM) to establish strong baselines for future research in this domain. The dataset is available at https://blog.thedeep.io/humset/.

HUMSET: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crisis Response

JamPatoisNLI provides the first dataset for natural language inference in a creole language, Jamaican Patois. Many of the most-spoken low-resource languages are creoles. These languages commonly have a lexicon derived from a major world language and a distinctive grammar reflecting the languages of the original speakers and the process of language birth by creolization. This gives them a distinctive place in exploring the effectiveness of transfer from large monolingual or multilingual pretrained models. While our work, along with previous work, shows that transfer from these models to low-resource languages that are unrelated to languages in their training set is not very effective, we would expect stronger results from transfer to creoles. Indeed, our experiments show considerably better results from few-shot learning of JamPatoisNLI than for such unrelated languages, and help us begin to understand how the unique relationship between creoles and their high-resource base languages affect cross-lingual transfer. JamPatoisNLI, which consists of naturally-occurring premises and expert-written hypotheses, is a step towards steering research into a traditionally underserved language and a useful benchmark for understanding cross-lingual NLP.

JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset

Zero-resource cross-lingual transfer approaches aim to apply supervised models
from a source language to unlabelled target languages. In this paper we perform
an in-depth study of the two main techniques employed so far for cross-lingual
zero-resource sequence labelling, based either on data or model transfer.
Although previous research has proposed translation and annotation projection
(data-based cross-lingual transfer) as an effective technique for cross-lingual
sequence labelling, in this paper we experimentally demonstrate that high
capacity multilingual language models applied in a zero-shot (model-based
cross-lingual transfer) setting consistently outperform data-based
cross-lingual transfer approaches. A detailed analysis of our results suggests
that this might be due to important differences in language use. More
specifically, machine translation often generates a textual signal which is
different to what the models are exposed to when using gold standard data,
which affects both the fine-tuning and evaluation processes. Our results also
indicate that data-based cross-lingual transfer approaches remain a competitive
option when high-capacity multilingual language models are not available.

Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings

This paper deals with the problem of incremental dialect identification. Our goal is to
reliably determine the dialect before the full utterance is given as input. The major part of the previous research on dialect identification has been model-centric, focusing on performance. We address a new question: How much input is needed to identify a dialect? Our approach is a data-centric analysis that results in general criteria for finding the shortest input needed to make a plausible guess. Working
with three sets of language dialects (Swiss German, Indo-Aryan and Arabic languages), we show that it is possible to generalize across dialects and datasets with two input shortening criteria: model confidence and minimal input length (adjusted for the input type).

Early Guessing for Dialect Identification

We propose EntityCS, a method that focuses on Entity-level Code-Switching to capture fine-grained cross-lingual semantics without corrupting syntax. We construct and release an EntityCS corpus with 93 languages based on English Wikipedia and Wikidata, and design novel masking strategies for entity prediction. XLM trained on the constructed EntityCS corpus with the proposed masking strategies shows consistent improvement on entity-centric downstream tasks.

EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching

Causal inference is becoming an increasingly important topic in deep learning, with the potential to help with critical deep learning problems such as model robustness, interpretability, and fairness. In addition, causality is naturally widely used in various disciplines of science, to discover causal relationships among variables and estimate causal effects of interest. In this tutorial, we introduce the fundamentals of causal discovery and causal effect estimation to the natural language processing (NLP) audience, provide an overview of causal perspectives to NLP problems, and aim to inspire novel approaches to NLP further. This tutorial is inclusive to a variety of audiences and is expected to facilitate the community’s developments in formulating and addressing new, important NLP problems in light of emerging causal principles and methodologies.

Tutorial on Causal Inference for Natural Language Processing

State-of-the-art language models in NLP perform best when fine-tuned even on small datasets, but due to their increasing size, fine-tuning and downstream usage have become extremely compute-intensive. Being able to efficiently and effectively fine-tune the largest pre-trained models is thus key in order to reap the benefits of the latest advances in NLP. In this tutorial, we provide a comprehensive overview of parameter-efficient fine-tuning methods. We highlight their similarities and differences by presenting them in a unified view. We explore the benefits and usage scenarios of a neglected property of such parameter-efficient models—modularity—such as composition of modules to deal with previously unseen data conditions. We finally highlight how both properties—parameter efficiency and modularity—can be useful in the real-world setting of adapting pre-trained models to under-represented languages and domains with scarce annotated data for several downstream applications. 

Modular and Parameter-Efficient Fine-Tuning for NLP Models

The autoregressive mechanism makes it convenient to train sequence generation models, but the generation is slow since autoregressive models can only generate the target sequence word-by-word. Non-autoregressive (NAR) models generate all tokens in parallel by removing the sequential dependencies within the target sequence. While NAR models have received much attention in sequence generation tasks, it is non-trivial for NAR models to obtain satisfactory performance, where the major challenge is to capture the multi-modal data distribution. In this tutorial, we will provide a comprehensive introduction to non-autoregressive sequence generation in four sections. First, we will briefly introduce the background of sequence generation, give the motivation of NAR generation and the multi-modality challenge faced by NAR models. Second, we will introduce efforts to improve NAR generation. We focus on non-autoregressive translation in this paper, which includes many representative methods from different aspects: training objectives, data distillation, latent modeling, upsampling, improved decoding approaches, etc. Third, we will introduce the application of NAR models on a wide range of sequence generation tasks, including language modeling, speech recognition, text-to-speech, etc. Finally, we will conclude this tutorial by summarizing the strengths and challenges of NAR models, and discussing current concerns and future directions of NAR generation. 


Improving Bilingual Lexicon Induction with Cross-Encoder Reranking

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The Effects of Corpus Choice and Morphosyntax on Multilingual Space Induction

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