Silver Retriever: Advancing Neural Passage Retrieval for Polish Question Answering (2024.lrec-main)
Copied to clipboard
| Challenge: | lexical approaches to find passages have outperformed lexicals due to their superior performance . however, for some languages, such as Polish, few models are available . a recent study shows that neural retrievers are more efficient and efficient than lexica. |
| Approach: | They present a neural retriever for Polish trained on a diverse collection of manual and weakly labeled datasets. |
| Outcome: | The proposed model outperforms lexical retrieval models in Polish on three retrieval tasks. |
Similar Papers
End-to-End Training of Neural Retrievers for Open-Domain Question Answering (2021.acl-long)
Copied to clipboard
Devendra Sachan, Mostofa Patwary, Mohammad Shoeybi, Neel Kant, Wei Ping, William L. Hamilton, Bryan Catanzaro
| Challenge: | Recent work on training neural retrievers for open-domain question answering (OpenQA) has employed both supervised and unsupervised methods. |
| Approach: | They propose an approach of unsupervised pre-training with the Inverse Cloze Task and masked salient spans followed by supervised finetuning using question-context pairs. |
| Outcome: | The proposed approach outperforms models like REALM and RAG in retrieval accuracy and answer extraction. |
Efficient Passage Retrieval with Hashing for Open-domain Question Answering (2021.acl-short)
Copied to clipboard
| Challenge: | Open-domain question answering systems often require large memory to run because of the massive size of their passage index. |
| Approach: | They propose a memory-efficient neural retrieval model that integrates a learning-to-hash technique into the state-of-the-art Dense Passage Retriever to represent the passage index using compact binary codes. |
| Outcome: | The proposed model significantly reduces memory cost from 65GB to 2GB without loss of accuracy on two open-domain question answering benchmarks. |
SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval (2021.naacl-main)
Copied to clipboard
| Challenge: | SPARTA is a novel neural retrieval method for open-domain question answering . it learns a sparse representation that can be efficiently implemented as an Inverted Index . |
| Approach: | They propose a method that learns a sparse representation that can be implemented as an Inverted Index. |
| Outcome: | The proposed method achieves state-of-the-art results on 4 open-domain question answering tasks and 11 retrieval question answering (ReQA) tasks. |
Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to open-domain question answering struggle to retrieve indirectly related evidence when no direct evidence is provided. |
| Approach: | They propose a retriever-reader model that learns to attend on essential terms during the question answering process. |
| Outcome: | The proposed model achieves the state-of-the-art on multiple open-domain QA datasets and achieves a 'reader-reader' level. |
Retrieval-based Question Answering with Passage Expansion Using a Knowledge Graph (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent advances in dense neural retrievers and language models have hindered performance, especially for less common entities and facts. |
| Approach: | They propose a multi-modal passage retrieval model that combines entity features and textual data to improve retrieval precision for less common entities. |
| Outcome: | The proposed model improves retrieval precision on less common entities and facts on common benchmarks. |
Answer Generation for Retrieval-based Question Answering Systems (2021.findings-acl)
Copied to clipboard
| Challenge: | Question Answering systems are a core component of many commercial applications . answer sentence selection (AS2) models are trained to select the best answer sentence . |
| Approach: | They propose to train a sequence to sequence transformer model to generate an answer from a set of candidates. |
| Outcome: | The proposed model improves accuracy by 32 points over the state-of-the-art model on English AS2 datasets. |
Neural Retriever and Go Beyond: A Thesis Proposal (2022.naacl-srw)
Copied to clipboard
| Challenge: | Existing neural retrievers are developed for pure-text queries, which prevents them from handling multi-modality queries. |
| Approach: | They propose methods to address issues of existing neural retrievers from three angles . they propose new model architectures, IR-oriented pretraining tasks and generating large scale training data . |
| Outcome: | The proposed methods address the abovementioned issues of neural retrievers from three angles and generate large scale training data. |
You Only Need One Model for Open-domain Question Answering (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent approaches to Open-domain Question Answering use external knowledge bases, but have separate parameters and are weakly-coupled during training. |
| Approach: | They propose to use a single question answering model trained end-to-end to retrieve external knowledge and rerank passages with a separate reranked model. |
| Outcome: | The proposed model outperforms the previous state-of-the-art model by 1.0 and 0.7 exact match scores on the Natural Questions and TriviaQA open datasets. |
PolQA: Polish Question Answering Dataset (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent proposed systems for open-domain question answering (OpenQA) require large amounts of training data to achieve state-of-the-art performance. |
| Approach: | They propose an efficient annotation strategy that increases passage retrieval accuracy@10 by 10.55 p.p. while reducing the annotation cost by 82%. |
| Outcome: | The proposed approach increases passage retrieval accuracy @10 by 10.55 p.p. while reducing the annotation cost by 82%. |
Answering Complex Open-domain Questions Through Iterative Query Generation (D19-1)
Copied to clipboard
| Challenge: | Currently, one-step retrieve-and-read question answering systems cannot answer such questions because they rarely contain retrievable clues about the missing entity. |
| Approach: | They propose a multi-step approach to retrieve relevant content with the question, then reading the paragraphs returned by the information retrieval component to arrive at the final answer. |
| Outcome: | The proposed model outperforms the best previously published model despite not using pretrained language models such as BERT. |