README.md

ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering System

Authors: Chia-Chien Hung, Tommaso Green, Robert Litschko, Tornike Tsereteli, Sotaro Takeshita, Marco Bombieri, Goran Glavaš, Simone Paolo Ponzetto

NAACL 2022. MIA Workshop: https://aclanthology.org/2022.mia-1.8/

Introduction

This paper introduces our proposed system for the MIA Shared Task on Cross-lingual Open-retrieval Question Answering (COQA). In this challenging scenario, given an input question the system has to gather evidence documents from a multilingual pool and generate from them an answer in the language of the question. We devised several approaches combining different model variants for three main components: Data Augmentation, Passage Retrieval, and Answer Generation. For passage retrieval, we evaluated the monolingual BM25 ranker against the ensemble of re-rankers based on multilingual pretrained language models (PLMs) and also variants of the shared task baseline, re-training it from scratch using a recently introduced contrastive loss that maintains a strong gradient signal throughout training by means of mixed negative samples. For answer generation, we focused on language- and domain-specialization by means of continued language model (LM) pretraining of existing multilingual encoders. Additionally, for both passage retrieval and answer generation, we augmented the training data provided by the task organizers with automatically generated question-answer pairs created from Wikipedia passages to mitigate the issue of data scarcity, particularly for the low-resource languages for which no training data were provided. Our results show that language- and domain-specialization as well as data augmentation help, especially for low-resource languages.

Overview of our proposed framework:

Thanks for the organizers from MIA-Shared-Task, our work is mainly modified from here.

Citation

If you use any source codes, or datasets included in this repo in your work, please cite the following paper:

@inproceedings{hung-etal-2022-zusammenqa,
    title = "{Z}usammen{QA}: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering System",
    author = "Hung, Chia-Chien  and
      Green, Tommaso  and
      Litschko, Robert  and
      Tsereteli, Tornike  and
      Takeshita, Sotaro  and
      Bombieri, Marco  and
      Glava{\v{s}}, Goran  and
      Ponzetto, Simone Paolo",
    booktitle = "Proceedings of the Workshop on Multilingual Information Access (MIA)",
    month = jul,
    year = "2022",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.mia-1.8",
    doi = "10.18653/v1/2022.mia-1.8",
    pages = "77--90"
}

Pretrained Models

The pre-trained models can be easily loaded using huggingface Transformers or Adapter-Hub adapter-transformers library using the AutoModel function. Following pre-trained versions are supported:

• umanlp/mt5-mlm-wiki14: mt5 pre-trained using MLM objective with concatenation of 14 languages WIKI corpora
• umanlp/mt5-mlm-16: mt5 pre-trained using MLM objective with concatenation of 14 languages WIKI corpora + 2 languages CCNet corpora

Or you can download it from umanlp/mt5-mlm-wiki14 and umanlp/mt5-mlm-16

Datasets

1. Augmented Data

Please check the data_augmentation folder for more detailed infos.

2. Corpora for mt5 language-, domain-specialization

Two datasets mt5-wiki14 and mt5-16 are created for intermediate training purpose, in order to encode knowledge via the domain- and language-specific corpus. You can simply download the data from mt5-wiki14 and mt5-16. Or you can modify the scripts under specialization folder for your own usage.

Structure

This repository is currently under the following structure:

.
└── baseline
    └── mDPR
    └── mGEN
    └── wikipedia_preprocess 
└── data
└── data_augmentation
└── eval_scripts
└── sample_predictions
└── specialization
└── img
└── README.md
└── orig-README.md