XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages

• URL: http://arxiv.org/abs/2202.00291v1
• Date: Tue, 1 Feb 2022 09:41:59 GMT
• Title: XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages
• Authors: Tushar Abhishek, Shivprasad Sagare, Bhavyajeet Singh, Anubhav Sharma, Manish Gupta and Vasudeva Varma
• Abstract summary: Multiple critical scenarios (like Wikipedia text generation given English Infoboxes) need automated generation of descriptive text in low resource (LR) languages from English fact triples. To the best of our knowledge, there has been no previous attempt on cross-lingual alignment or generation for LR languages. We propose two unsupervised methods for cross-lingual alignment.
• Score: 11.581072296148031
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multiple critical scenarios (like Wikipedia text generation given English Infoboxes) need automated generation of descriptive text in low resource (LR) languages from English fact triples. Previous work has focused on English fact-to-text (F2T) generation. To the best of our knowledge, there has been no previous attempt on cross-lingual alignment or generation for LR languages. Building an effective cross-lingual F2T (XF2T) system requires alignment between English structured facts and LR sentences. We propose two unsupervised methods for cross-lingual alignment. We contribute XALIGN, an XF2T dataset with 0.45M pairs across 8 languages, of which 5402 pairs have been manually annotated. We also train strong baseline XF2T generation models on the XAlign dataset.

Related papers

• Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages [90.41827664700847]
  We propose Cross-Lingual Knowledge Distillation (CLKD) from a strong English AS2 teacher as a method to train AS2 models for low-resource languages. To evaluate our method, we introduce 1) Xtr-WikiQA, a translation-based WikiQA dataset for 9 additional languages, and 2) TyDi-AS2, a multilingual AS2 dataset with over 70K questions spanning 8 typologically diverse languages.
  arXiv  Detail & Related papers  (2023-05-25T17:56:04Z)
• 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)
• XNLI 2.0: Improving XNLI dataset and performance on Cross Lingual Understanding (XLU) [0.0]
  We focus on improving the original XNLI dataset by re-translating the MNLI dataset in all of the 14 different languages present in XNLI. We also perform experiments by training models in all 15 languages and analyzing their performance on the task of natural language inference.
  arXiv  Detail & Related papers  (2023-01-16T17:24:57Z)
• XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing [70.40401197026925]
  In-context learning using large language models has recently shown surprising results for semantic parsing tasks. This work introduces the XRICL framework, which learns to retrieve relevant English exemplars for a given query. We also include global translation exemplars for a target language to facilitate the translation process for large language models.
  arXiv  Detail & Related papers  (2022-10-25T01:33:49Z)
• XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languages [11.581072296148031]
  We conduct an extensive study using popular Transformer-based text generation models on our extended multi-lingual dataset. Our experiments show that a multi-lingual mT5 model which uses fact-aware embeddings with structure-aware input encoding leads to best results on average across the twelve languages.
  arXiv  Detail & Related papers  (2022-09-22T18:01:27Z)
• CONCRETE: Improving Cross-lingual Fact-checking with Cross-lingual Retrieval [73.48591773882052]
  Most fact-checking approaches focus on English only due to the data scarcity issue in other languages. We present the first fact-checking framework augmented with crosslingual retrieval. We train the retriever with our proposed Crosslingual Inverse Cloze Task (XICT)
  arXiv  Detail & Related papers  (2022-09-05T17:36:14Z)
• Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference [11.096793445651313]
  We investigate the cross-lingual transfer abilities of XLM-R for Chinese and English natural language inference (NLI) To better understand linguistic transfer, we created 4 categories of challenge and adversarial tasks for Chinese. We find that cross-lingual models trained on English NLI do transfer well across our Chinese tasks.
  arXiv  Detail & Related papers  (2021-06-07T22:00:18Z)
• XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning [68.57658225995966]
  Cross-lingual Choice of Plausible Alternatives (XCOPA) is a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages. We evaluate a range of state-of-the-art models on this novel dataset, revealing that the performance of current methods falls short compared to translation-based transfer.
  arXiv  Detail & Related papers  (2020-05-01T12:22:33Z)
• XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation [100.09099800591822]
  XGLUE is a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models. XGLUE provides 11 diversified tasks that cover both natural language understanding and generation scenarios.
  arXiv  Detail & Related papers  (2020-04-03T07:03:12Z)

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.