Papers with question answering model
KGI: An Integrated Framework for Knowledge Intensive Language Tasks (2022.emnlp-demos)
Copied to clipboard
Md Faisal Mahbub Chowdhury, Michael Glass, Gaetano Rossiello, Alfio Gliozzo, Nandana Mihindukulasooriya
| Challenge: | Existing state-of-the-art retrieval augmented generation models are not available for knowledge-intensive language tasks. |
| Approach: | They propose a retrieval augmented generation system that showcases the latest state-of-the-art retrieval models on knowledge-intensive language tasks. |
| Outcome: | The proposed system is based on the core of the KGI system. |
Exploring Compositional Image Retrieval with Hybrid Compositional Learning and Heuristic Negative Mining (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing CIR models are pre-trained on uni-modal data, resulting in unimodal data. |
| Approach: | They propose a CIR model HyCoLe-HNM with CLIP as the backbone . they use a gated fusion mechanism from a question answering model to perform compositional learning . |
| Outcome: | The proposed model achieves state-of-the-art performance on three CIR datasets . it borrows a gated fusion mechanism from a question answering model to perform compositional fusion . |
Learning to Generate Questions by Learning to Recover Answer-containing Sentences (2021.findings-acl)
Copied to clipboard
| Challenge: | Recent research has focused on synthetically generating a question from a given context and an annotated answer by training an additional generative model. |
| Approach: | They propose a method that learns to generate contextually rich questions by recovering answer-containing sentences. |
| Outcome: | The proposed approach improves the quality and accuracy of existing models and achieves comparable results to the state-of-the-art on MS MARCO and NewsQA. |
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. |
Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance (2021.findings-acl)
Copied to clipboard
| Challenge: | Query focused summarization models aim to generate summaries from source documents that can answer the given query. |
| Approach: | They propose a QFS-BART model that incorporates the explicit answer relevance of the source documents given the query via a question answering model. |
| Outcome: | Empirical results show that the proposed model achieves the new state-of-the-art performance. |
Answer Quality Aware Aggregation for Extractive QA Crowdsourcing (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for creating extractive question answering datasets are crowdsourcing, but results are often inconsistent. |
| Approach: | They propose a method for aggregating answers from different crowd workers that takes into account the relations between the answer, question, and context passage. |
| Outcome: | The proposed method outperforms baselines by 16% on precision and effectively conduct answer aggregation for extractive question answering task. |
Do NLP Models Know Numbers? Probing Numeracy in Embeddings (D19-1)
Copied to clipboard
| Challenge: | Existing models cannot capture numeracy, but they can be useful for complex reasoning tasks. |
| Approach: | They investigate numerical reasoning capabilities of a question-answering model . they probe token embedding methods on synthetic list maximum, number decoding, and addition tasks. |
| Outcome: | The proposed model excels on questions that require numerical reasoning, i.e., it already captures numeracy. |
CapWAP: Image Captioning with a Purpose (2020.emnlp-main)
Copied to clipboard
| Challenge: | a traditional image captioning task uses generic reference captions to provide textual information about images. |
| Approach: | They propose a task that uses question-answer pairs to provide visual information instead of generic reference captions. |
| Outcome: | The proposed captioning with a purpose task can be tailored to meet user needs . question-answer pairs are used as a source of supervision for learning visual information needs a new task is proposed . |
1-PAGER: One Pass Answer Generation and Evidence Retrieval (2023.findings-emnlp)
Copied to clipboard
| Challenge: | 1-Pager is the first system that answers a question and retrieves evidence using a single Transformer-based model and decoding process. |
| Approach: | They propose a system that partitions the corpus using constrained decoding to select a document and answer string, and a method that uses a single Transformer-based model to generate evidence. |
| Outcome: | The proposed system outperforms the equivalent ‘closed-book’ question answering model by grounding predictions in evidence corpus. |