Retrieval-based language models are increasingly employed in
question-answering tasks. These models search in a corpus of documents for
relevant information instead of having all factual knowledge stored in its
parameters, thereby enhancing efficiency, transparency, and adaptability. We
develop the first Norwegian retrieval-based model by adapting the REALM
framework and evaluating it on various tasks. After training, we also separate
the language model, which we call the reader, from the retriever components,
and show that this can be fine-tuned on a range of downstream tasks. Results
show that retrieval augmented language modeling improves the reader's
performance on extractive question-answering, suggesting that this type of
training improves language models' general ability to use context and that this
does not happen at the expense of other abilities such as part-of-speech
tagging, dependency parsing, named entity recognition, and lemmatization. Code,
trained models, and data are made publicly available.

利用 REALM 框架进行适应性评估，开发了第一个挪威基于检索的语言模型，证明了检索增强语言建模可提高读者在摘要问答上的表现，并表明这种类型的训练不会以其他功能为代价。

BRENT: 双向检索增强挪威变压器

BRENT: Bidirectional Retrieval Enhanced Norwegian Transformer

Indic languages like Hindi and Tamil are underrepresented in the natural
language processing (NLP) field compared to languages like English. Due to this
underrepresentation, performance on NLP tasks (such as search algorithms) in
Indic languages are inferior to their English counterparts. This difference
disproportionately affects those who come from lower socioeconomic statuses
because they consume the most Internet content in local languages. The goal of
this project is to build an NLP model that performs better than pre-existing
models for the task of extractive question-answering (QA) on a public dataset
in Hindi and Tamil. Extractive QA is an NLP task where answers to questions are
extracted from a corresponding body of text. To build the best solution, we
used three different models. The first model is an unmodified cross-lingual
version of the NLP model RoBERTa, known as XLM-RoBERTa, that is pretrained on
100 languages. The second model is based on the pretrained RoBERTa model with
an extra classification head for the question answering, but we used a custom
Indic tokenizer, then optimized hyperparameters and fine tuned on the Indic
dataset. The third model is based on XLM-RoBERTa, but with extra finetuning and
training on the Indic dataset. We hypothesize the third model will perform best
because of the variety of languages the XLM-RoBERTa model has been pretrained
on and the additional finetuning on the Indic dataset. This hypothesis was
proven wrong because the paired RoBERTa models performed the best as the
training data used was most specific to the task performed as opposed to the
XLM-RoBERTa models which had much data that was not in either Hindi or Tamil.

本篇研究旨在建立自然语言处理模型，通过针对印度语的公共数据集进行微调以及训练，使得机器的提取问答任务的表现比已有模型更为优秀。基于 RoBERTa 模型构建的两种模型表现最好，证实了对于特定语言任务而言，训练数据的特异性对模型的表现影响更大。

在印地语和泰米尔语中针对问题的抽取式问答

Extractive Question Answering on Queries in Hindi and Tamil

Background: Extractive question-answering (EQA) is a useful natural language
processing (NLP) application for answering patient-specific questions by
locating answers in their clinical notes. Realistic clinical EQA can have
multiple answers to a single question and multiple focus points in one
question, which are lacking in the existing datasets for development of
artificial intelligence solutions. Objective: Create a dataset for developing
and evaluating clinical EQA systems that can handle natural multi-answer and
multi-focus questions. Methods: We leveraged the annotated relations from the
2018 National NLP Clinical Challenges (n2c2) corpus to generate an EQA dataset.
Specifically, the 1-to-N, M-to-1, and M-to-N drug-reason relations were
included to form the multi-answer and multi-focus QA entries, which represent
more complex and natural challenges in addition to the basic
one-drug-one-reason cases. A baseline solution was developed and tested on the
dataset. Results: The derived RxWhyQA dataset contains 96,939 QA entries. Among
the answerable questions, 25% require multiple answers, and 2% ask about
multiple drugs within one question. There are frequent cues observed around the
answers in the text, and 90% of the drug and reason terms occur within the same
or an adjacent sentence. The baseline EQA solution achieved a best f1-measure
of 0.72 on the entire dataset, and on specific subsets, it was: 0.93 on the
unanswerable questions, 0.48 on single-drug questions versus 0.60 on multi-drug
questions, 0.54 on the single-answer questions versus 0.43 on multi-answer
questions. Discussion: The RxWhyQA dataset can be used to train and evaluate
systems that need to handle multi-answer and multi-focus questions.
Specifically, multi-answer EQA appears to be challenging and therefore warrants
more investment in research.

本文旨在开发一个新的数据集，用于研发和评估临床试验问题回答系统，该系统可以处理自然的多答案和多焦点问题。通过使用 2018 年国家 NLP 临床挑战（n2c2）语料库进行注释关系，包含 1 到 N、M 到 1 和 M 到 N 药品 - 事件关系，形成多答案和多焦点的 QA 项目，可以更好地挖掘围绕答案的线索，培训和评估需要处理多重答案和多重焦点问题的系统。