Open-domain surface-based question answering system

This paper considers a surface-based question answering system for an open- domain solution. It analyzes the current progress that has been done in this area so far, while as well describes a methodology of answering questions by using information retrieved from very large collection of text. The solution proposed is based on indexing techniques and surface-based natural language processing that identify paragraphs from which an answer can be extracted. Although this approach would not solve all the problems associated with this task the objective is to provide a solution that is feasible, achieves reasonable accuracy and can return an answer in an acceptable time limit. Various techniques are discussed including question analysis, question reformulation, term extraction, answer extraction and other methods for answer pinpointing. Besides this further research in question answering is identified, especially in the area of handling answers that require reasoning.peer-reviewe

Context Generation Improves Open Domain Question Answering

Closed-book question answering (QA) requires a model to directly answer an open-domain question without access to any external knowledge. Prior work on closed-book QA either directly finetunes or prompts a pretrained language model (LM) to leverage the stored knowledge. However, they do not fully exploit the parameterized knowledge. To address this issue, we propose a two-stage, closed-book QA framework which employs a coarse-to-fine approach to extract relevant knowledge and answer a question. Our approach first generates a related context for a given question by prompting a pretrained LM. We then prompt the same LM for answer prediction using the generated context and the question. Additionally, to eliminate failure caused by context uncertainty, we marginalize over generated contexts. Experimental results on three QA benchmarks show that our method significantly outperforms previous closed-book QA methods (e.g. exact matching 68.6% vs. 55.3%), and is on par with open-book methods that exploit external knowledge sources (e.g. 68.6% vs. 68.0%). Our method is able to better exploit the stored knowledge in pretrained LMs without adding extra learnable parameters or needing finetuning, and paves the way for hybrid models that integrate pretrained LMs with external knowledge.Comment: 8 pages; Accepted at EACL202