XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations

• URL: http://arxiv.org/abs/2306.04085v1
• Date: Wed, 7 Jun 2023 01:09:37 GMT
• Title: XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations
• Authors: Yusen Zhang, Jun Wang, Zhiguo Wang, Rui Zhang
• Abstract summary: Cross-Lingual Semantic Parsing aims to translate queries in multiple natural languages into meaning representations. Existing CLSP models are separately proposed and evaluated on datasets of limited tasks and applications. We present XSemPLR, a unified benchmark for cross-lingual semantic parsing featured with 22 natural languages and 8 meaning representations.
• Score: 25.50509874992198
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cross-Lingual Semantic Parsing (CLSP) aims to translate queries in multiple natural languages (NLs) into meaning representations (MRs) such as SQL, lambda calculus, and logic forms. However, existing CLSP models are separately proposed and evaluated on datasets of limited tasks and applications, impeding a comprehensive and unified evaluation of CLSP on a diverse range of NLs and MRs. To this end, we present XSemPLR, a unified benchmark for cross-lingual semantic parsing featured with 22 natural languages and 8 meaning representations by examining and selecting 9 existing datasets to cover 5 tasks and 164 domains. We use XSemPLR to conduct a comprehensive benchmark study on a wide range of multilingual language models including encoder-based models (mBERT, XLM-R), encoder-decoder models (mBART, mT5), and decoder-based models (Codex, BLOOM). We design 6 experiment settings covering various lingual combinations (monolingual, multilingual, cross-lingual) and numbers of learning samples (full dataset, few-shot, and zero-shot). Our experiments show that encoder-decoder models (mT5) achieve the highest performance compared with other popular models, and multilingual training can further improve the average performance. Notably, multilingual large language models (e.g., BLOOM) are still inadequate to perform CLSP tasks. We also find that the performance gap between monolingual training and cross-lingual transfer learning is still significant for multilingual models, though it can be mitigated by cross-lingual few-shot training. Our dataset and code are available at https://github.com/psunlpgroup/XSemPLR.

Related papers

• Think Carefully and Check Again! Meta-Generation Unlocking LLMs for Low-Resource Cross-Lingual Summarization [108.6908427615402]
  Cross-lingual summarization ( CLS) aims to generate a summary for the source text in a different target language. Currently, instruction-tuned large language models (LLMs) excel at various English tasks. Recent studies have shown that LLMs' performance on CLS tasks remains unsatisfactory even with few-shot settings.
  arXiv  Detail & Related papers  (2024-10-26T00:39:44Z)
• Large Language Models for cross-language code clone detection [3.5202378300682162]
  Cross-lingual code clone detection has gained traction with the software engineering community. Inspired by the significant advances in machine learning, this paper revisits cross-lingual code clone detection.
  arXiv  Detail & Related papers  (2024-08-08T12:57:14Z)
• Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning [98.60739735409243]
  Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks. We introduce XSGD for cross-lingual alignment pretraining, a parallel and large-scale multilingual conversation dataset. To facilitate aligned cross-lingual representations, we develop an efficient prompt-tuning-based method for learning alignment prompts.
  arXiv  Detail & Related papers  (2023-04-03T18:46:01Z)
• Multi-lingual Evaluation of Code Generation Models [82.7357812992118]
  We present new benchmarks on evaluation code generation models: MBXP and Multilingual HumanEval, and MathQA-X. These datasets cover over 10 programming languages. We are able to assess the performance of code generation models in a multi-lingual fashion.
  arXiv  Detail & Related papers  (2022-10-26T17:17:06Z)
• Cross-Lingual Text Classification with Multilingual Distillation and Zero-Shot-Aware Training [21.934439663979663]
  Multi-branch multilingual language model (MBLM) built on Multilingual pre-trained language models (MPLMs) Method based on transferring knowledge from high-performance monolingual models with a teacher-student framework. Results on two cross-lingual classification tasks show that, with only the task's supervised data used, our method improves both the supervised and zero-shot performance of MPLMs.
  arXiv  Detail & Related papers  (2022-02-28T09:51:32Z)
• A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification [16.684856745734944]
  We present a multilingual bag-of-entities model that boosts the performance of zero-shot cross-lingual text classification. It leverages the multilingual nature of Wikidata: entities in multiple languages representing the same concept are defined with a unique identifier. A model trained on entity features in a resource-rich language can thus be directly applied to other languages.
  arXiv  Detail & Related papers  (2021-10-15T01:10:50Z)
• CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP [68.2650714613869]
  We propose a data augmentation framework to generate multi-lingual code-switching data to fine-tune mBERT. Compared with the existing work, our method does not rely on bilingual sentences for training, and requires only one training process for multiple target languages.
  arXiv  Detail & Related papers  (2020-06-11T13:15:59Z)
• GLUECoS : An Evaluation Benchmark for Code-Switched NLP [17.066725832825423]
  We present an evaluation benchmark, GLUECoS, for code-switched languages. We present results on several NLP tasks in English-Hindi and English-Spanish. We fine-tune multilingual models on artificially generated code-switched data.
  arXiv  Detail & Related papers  (2020-04-26T13:28:34Z)
• Learning to Scale Multilingual Representations for Vision-Language Tasks [51.27839182889422]
  The effectiveness of SMALR is demonstrated with ten diverse languages, over twice the number supported in vision-language tasks to date. We evaluate on multilingual image-sentence retrieval and outperform prior work by 3-4% with less than 1/5th the training parameters compared to other word embedding methods.
  arXiv  Detail & Related papers  (2020-04-09T01:03:44Z)
• XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization [128.37244072182506]
  Cross-lingual TRansfer Evaluation of Multilinguals XTREME is a benchmark for evaluating the cross-lingual generalization capabilities of multilingual representations across 40 languages and 9 tasks. We demonstrate that while models tested on English reach human performance on many tasks, there is still a sizable gap in the performance of cross-lingually transferred models.
  arXiv  Detail & Related papers  (2020-03-24T19:09:37Z)

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.