Papers with Open Domain Question Answering
Cross-document Event Coreference Search: Task, Dataset and Modeling (2022.emnlp-main)
Copied to clipboard
| Challenge: | Cross-document Event Coreference resolution is the task of identifying clusters of text mentions that refer to the same event, whether within a single document or across a document collection. |
| Approach: | They propose a cross-document coreference search task that searches for all coreferring mentions for a query event in a large document collection. |
| Outcome: | The proposed model integrates a powerful coreference scoring scheme into the DPR architecture, yielding improved performance. |
Challenges in Generalization in Open Domain Question Answering (2022.findings-naacl)
Copied to clipboard
| Challenge: | Recent work on Open Domain Question Answering has shown that there is a large discrepancy in model performance between novel test questions and those that largely overlap with training questions. |
| Approach: | They introduce and annotate questions according to three categories that measure training set overlap, compositional generalization, and novel-entity generalization. |
| Outcome: | The proposed models perform better on established datasets and lower on comp-gen/novel-entity questions than on the full test set. |
FiE: Building a Global Probability Space by Leveraging Early Fusion in Encoder for Open-Domain Question Answering (2022.emnlp-main)
Copied to clipboard
| Challenge: | generative models tend to be larger than extractive models due to the need for a decoder, run slower during inference due to auto-regressive decoded beam search, and their generated output suffers from hallucinations. |
| Approach: | They propose to extend transformer encoders with the ability to fuse information from multiple passages to provide cross-sample attention over all tokens across samples. |
| Outcome: | The proposed method outperforms the current state-of-the-art method by 2.5 Exact Match score on the Natural Question dataset while using only 25% of parameters and 35% of the latency during inference. |
Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Retrieval-Augmented Large Language Models (RALMs) do not consistently outperform the original retrieval-free Language Model (LM). |
| Approach: | They propose a trainable framework that can adaptively retrieve from different knowledge sources and effectively decrease unpredictable reader errors. |
| Outcome: | The proposed framework significantly improves performance over the RALM with a single retriever by significantly reducing inconsistent behaviors. |
FastFiD: Improve Inference Efficiency of Open Domain Question Answering via Sentence Selection (2024.acl-long)
Copied to clipboard
| Challenge: | Open Domain Question Answering (ODQA) is a longstanding task in Natural Language Processing that involves generating an answer solely based on a given question. |
| Approach: | They propose a novel approach that executes sentence selection on the encoded passages to enhance the inference speed while reducing the context length required for generating answers. |
| Outcome: | The proposed approach can increase inference speed by **2.3X-5.7X** while maintaining the model’s performance. |
Strong and Efficient Baselines for Open Domain Conversational Question Answering (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Current Open Domain Question Answering (ODQA) approaches to reevaluating baselines for efficiency and effectiveness have been limited due to various limitations. |
| Approach: | They propose and evaluate strong yet simple and efficient baselines by introducing a fast reranking component between the retriever and the reader and performing targeted finetuning steps. |
| Outcome: | The proposed method improves the SotA results while reducing the reader’s latency by 60%. |
Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text (D18-1)
Copied to clipboard
| Challenge: | Specialized neural models have been developed for extracting answers from text alone or Knowledge Bases (KBs) alone. |
| Approach: | They propose a novel model for extracting answers from a question-specific subgraph containing text and KB entities and relations. |
| Outcome: | The proposed model outperforms existing methods in a combination of a KB and entity-linked text in QA over a large text corpus. |