To token or not to token: A Comparative Study of Text Representations for Cross-Lingual Transfer

• URL: http://arxiv.org/abs/2310.08078v1
• Date: Thu, 12 Oct 2023 06:59:10 GMT
• Title: To token or not to token: A Comparative Study of Text Representations for Cross-Lingual Transfer
• Authors: Md Mushfiqur Rahman, Fardin Ahsan Sakib, Fahim Faisal, Antonios Anastasopoulos
• Abstract summary: We propose a scoring Language Quotient metric capable of providing a weighted representation of both zero-shot and few-shot evaluation combined. Our analysis reveals that image-based models excel in cross-lingual transfer when languages are closely related and share visually similar scripts. In dependency parsing tasks where word relationships play a crucial role, models with their character-level focus, outperform others.
• Score: 23.777874316083984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Choosing an appropriate tokenization scheme is often a bottleneck in low-resource cross-lingual transfer. To understand the downstream implications of text representation choices, we perform a comparative analysis on language models having diverse text representation modalities including 2 segmentation-based models (\texttt{BERT}, \texttt{mBERT}), 1 image-based model (\texttt{PIXEL}), and 1 character-level model (\texttt{CANINE}). First, we propose a scoring Language Quotient (LQ) metric capable of providing a weighted representation of both zero-shot and few-shot evaluation combined. Utilizing this metric, we perform experiments comprising 19 source languages and 133 target languages on three tasks (POS tagging, Dependency parsing, and NER). Our analysis reveals that image-based models excel in cross-lingual transfer when languages are closely related and share visually similar scripts. However, for tasks biased toward word meaning (POS, NER), segmentation-based models prove to be superior. Furthermore, in dependency parsing tasks where word relationships play a crucial role, models with their character-level focus, outperform others. Finally, we propose a recommendation scheme based on our findings to guide model selection according to task and language requirements.

Related papers

• Exploring Multiple Strategies to Improve Multilingual Coreference Resolution in CorefUD [0.0]
  This paper presents our end-to-end neural coreference resolution system. We first establish strong baseline models, including monolingual and cross-lingual variations. We propose several extensions to enhance performance across diverse linguistic contexts.
  arXiv  Detail & Related papers  (2024-08-29T20:27:05Z)
• Multilingual Conceptual Coverage in Text-to-Image Models [98.80343331645626]
  "Conceptual Coverage Across Languages" (CoCo-CroLa) is a technique for benchmarking the degree to which any generative text-to-image system provides multilingual parity to its training language in terms of tangible nouns. For each model we can assess "conceptual coverage" of a given target language relative to a source language by comparing the population of images generated for a series of tangible nouns in the source language to the population of images generated for each noun under translation in the target language.
  arXiv  Detail & Related papers  (2023-06-02T17:59:09Z)
• Analyzing Vietnamese Legal Questions Using Deep Neural Networks with Biaffine Classifiers [3.116035935327534]
  We propose using deep neural networks to extract important information from Vietnamese legal questions. Given a legal question in natural language, the goal is to extract all the segments that contain the needed information to answer the question.
  arXiv  Detail & Related papers  (2023-04-27T18:19:24Z)
• Incorporating Linguistic Knowledge for Abstractive Multi-document Summarization [20.572283625521784]
  We develop a neural network based abstractive multi-document summarization (MDS) model. We process the dependency information into the linguistic-guided attention mechanism. With the help of linguistic signals, sentence-level relations can be correctly captured.
  arXiv  Detail & Related papers  (2021-09-23T08:13:35Z)
• X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing [51.81533991497547]
  Task-oriented compositional semantic parsing (TCSP) handles complex nested user queries. We present X2 compared a transferable Cross-lingual and Cross-domain for TCSP. We propose to predict flattened intents and slots representations separately and cast both prediction tasks into sequence labeling problems.
  arXiv  Detail & Related papers  (2021-06-07T16:40:05Z)
• Exemplar-Controllable Paraphrasing and Translation using Bitext [57.92051459102902]
  We adapt models from prior work to be able to learn solely from bilingual text (bitext) Our single proposed model can perform four tasks: controlled paraphrase generation in both languages and controlled machine translation in both language directions.
  arXiv  Detail & Related papers  (2020-10-12T17:02:50Z)
• Cross-lingual Spoken Language Understanding with Regularized Representation Alignment [71.53159402053392]
  We propose a regularization approach to align word-level and sentence-level representations across languages without any external resource. Experiments on the cross-lingual spoken language understanding task show that our model outperforms current state-of-the-art methods in both few-shot and zero-shot scenarios.
  arXiv  Detail & Related papers  (2020-09-30T08:56:53Z)
• Towards Instance-Level Parser Selection for Cross-Lingual Transfer of Dependency Parsers [59.345145623931636]
  We argue for a novel cross-lingual transfer paradigm: instance-level selection (ILPS) We present a proof-of-concept study focused on instance-level selection in the framework of delexicalized transfer.
  arXiv  Detail & Related papers  (2020-04-16T13:18:55Z)
• Coreferential Reasoning Learning for Language Representation [88.14248323659267]
  We present CorefBERT, a novel language representation model that can capture the coreferential relations in context. The experimental results show that, compared with existing baseline models, CorefBERT can achieve significant improvements consistently on various downstream NLP tasks.
  arXiv  Detail & Related papers  (2020-04-15T03:57:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.