{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/open-domain-question-answering/papers/4","list_of":"/task/open-domain-question-answering","task":"Open-Domain Question Answering","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":4,"pages_in_order":5,"rows_per_page":100,"rows":[301,400],"of":494,"counts":{"archive_papers_tagged":494,"with_a_code_link":238,"where_syntology_ran_a_sample":80,"not_listed_spam_title":0,"listed":494,"listed_where_code_ran":80,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":68,"every_run_a_failure_of_syntologys_instrument":12,"listed_with_a_run_with_no_instrument_failure":68,"listed_every_run_a_failure_of_syntologys_instrument":12,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/open-domain-question-answering","prev":"/task/open-domain-question-answering/papers/3","next":"/task/open-domain-question-answering/papers/5","papers":[{"url":null,"slug":"avatar-robust-voice-search-engine-leveraging","title":"AVATAR: Robust Voice Search Engine Leveraging Autoregressive Document Retrieval and Contrastive Learning","date":"2023-09-04","arxiv_id":"2309.01395","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-integration-strategies-of","title":"Modeling Uncertainty and Using Post-fusion as Fallback Improves Retrieval Augmented Generation with LLMs","date":"2023-08-24","arxiv_id":"2308.12574","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-interpretable-and-reliable-open","title":"Building Interpretable and Reliable Open Information Retriever for New Domains Overnight","date":"2023-08-09","arxiv_id":"2308.04756","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieving-supporting-evidence-for-llms","title":"Retrieving Supporting Evidence for LLMs Generated Answers","date":"2023-06-23","arxiv_id":"2306.13781","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-to-read-documents-or-qa-history-on","title":"When to Read Documents or QA History: On Unified and Selective Open-domain QA","date":"2023-06-07","arxiv_id":"2306.04176","repositories_listed":0,"syntology":null},{"url":null,"slug":"question-context-alignment-and-answer-context","title":"Question-Context Alignment and Answer-Context Dependencies for Effective Answer Sentence Selection","date":"2023-06-03","arxiv_id":"2306.02196","repositories_listed":0,"syntology":null},{"url":null,"slug":"griprank-bridging-the-gap-between-retrieval","title":"GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking","date":"2023-05-29","arxiv_id":"2305.18144","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-dangers-of-trusting-stochastic-parrots","title":"The Dangers of trusting Stochastic Parrots: Faithfulness and Trust in Open-domain Conversational Question Answering","date":"2023-05-25","arxiv_id":"2305.16519","repositories_listed":0,"syntology":null},{"url":null,"slug":"ifqa-a-dataset-for-open-domain-question","title":"IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions","date":"2023-05-23","arxiv_id":"2305.14010","repositories_listed":0,"syntology":null},{"url":null,"slug":"maupqa-massive-automatically-created-polish","title":"MAUPQA: Massive Automatically-created Polish Question Answering Dataset","date":"2023-05-09","arxiv_id":"2305.05486","repositories_listed":0,"syntology":null},{"url":null,"slug":"why-does-chatgpt-fall-short-in-answering","title":"Why Does ChatGPT Fall Short in Providing Truthful Answers?","date":"2023-04-20","arxiv_id":"2304.10513","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-dense-retrieval-with-unanswerable","title":"Evidentiality-aware Retrieval for Overcoming Abstractiveness in Open-Domain Question Answering","date":"2023-04-06","arxiv_id":"2304.03031","repositories_listed":0,"syntology":null},{"url":null,"slug":"check-your-facts-and-try-again-improving","title":"Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback","date":"2023-02-24","arxiv_id":"2302.12813","repositories_listed":0,"syntology":null},{"url":"/paper/rlas-biabc-a-reinforcement-learning-based","slug":"rlas-biabc-a-reinforcement-learning-based","title":"RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithm","date":"2023-01-07","arxiv_id":"2301.02807","repositories_listed":0,"syntology":null},{"url":null,"slug":"to-adapt-or-to-annotate-challenges-and","title":"To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering","date":"2022-12-20","arxiv_id":"2212.10381","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-question-answering-performance-1","title":"PolQA: Polish Question Answering Dataset","date":"2022-12-17","arxiv_id":"2212.08897","repositories_listed":0,"syntology":null},{"url":null,"slug":"diverse-multi-answer-retrieval-with-1","title":"Diverse Multi-Answer Retrieval with Determinantal Point Processes","date":"2022-11-29","arxiv_id":"2211.16029","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-open-domain-qa-reader-utilize-external","title":"Can Open-Domain QA Reader Utilize External Knowledge Efficiently like Humans?","date":"2022-11-23","arxiv_id":"2211.12707","repositories_listed":0,"syntology":null},{"url":"/paper/fie-building-a-global-probability-space-by","slug":"fie-building-a-global-probability-space-by","title":"FiE: Building a Global Probability Space by Leveraging Early Fusion in Encoder for Open-Domain Question Answering","date":"2022-11-18","arxiv_id":"2211.10147","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-for-efficient-open-domain-question","title":"A Survey for Efficient Open Domain Question Answering","date":"2022-11-15","arxiv_id":"2211.07886","repositories_listed":0,"syntology":null},{"url":null,"slug":"cheater-s-bowl-human-vs-computer-search","title":"Cheater's Bowl: Human vs. Computer Search Strategies for Open-Domain Question Answering","date":"2022-11-15","arxiv_id":"2212.03296","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-the-training-inference-gap-for-dense","title":"Bridging the Training-Inference Gap for Dense Phrase Retrieval","date":"2022-10-25","arxiv_id":"2210.13678","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-generation-improves-open-domain","title":"Context Generation Improves Open Domain Question Answering","date":"2022-10-12","arxiv_id":"2210.06349","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoupled-context-processing-for-context","title":"Decoupled Context Processing for Context Augmented Language Modeling","date":"2022-10-11","arxiv_id":"2210.05758","repositories_listed":0,"syntology":null},{"url":null,"slug":"fid-light-efficient-and-effective-retrieval","title":"FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation","date":"2022-09-28","arxiv_id":"2209.14290","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-methods-for-natural-language","title":"Efficient Methods for Natural Language Processing: A Survey","date":"2022-08-31","arxiv_id":"2209.00099","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-resource-dense-retrieval-for-open-domain","title":"Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey","date":"2022-08-05","arxiv_id":"2208.03197","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-retrieval-augmented-text","title":"Multi-Task Retrieval-Augmented Text Generation with Relevance Sampling","date":"2022-07-07","arxiv_id":"2207.03030","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-encoder-attribution-analysis-for-dense-1","title":"An Encoder Attribution Analysis for Dense Passage Retriever in Open-Domain Question Answering","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-hop-open-domain-question-answering-over","title":"Multi-Hop Open-Domain Question Answering over Structured and Unstructured Knowledge","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-cross-lingual-open-domain-question","title":"Zero-shot cross-lingual open domain question answering","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-open-domain-qa-system-for-e-governance","title":"An Open-Domain QA System for e-Governance","date":"2022-06-16","arxiv_id":"2206.08046","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-retriever-and-go-beyond-a-thesis","title":"Neural Retriever and Go Beyond: A Thesis Proposal","date":"2022-05-31","arxiv_id":"2205.16005","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-information-inconsistency-in","title":"Investigating Information Inconsistency in Multilingual Open-Domain Question Answering","date":"2022-05-25","arxiv_id":"2205.12456","repositories_listed":0,"syntology":null},{"url":"/paper/a-survey-on-neural-open-information","slug":"a-survey-on-neural-open-information","title":"A Survey on Neural Open Information Extraction: Current Status and Future Directions","date":"2022-05-24","arxiv_id":"2205.11725","repositories_listed":0,"syntology":null},{"url":null,"slug":"ernie-search-bridging-cross-encoder-with-dual","title":"ERNIE-Search: Bridging Cross-Encoder with Dual-Encoder via Self On-the-fly Distillation for Dense Passage Retrieval","date":"2022-05-18","arxiv_id":"2205.09153","repositories_listed":0,"syntology":null},{"url":null,"slug":"better-retrieval-may-not-lead-to-better","title":"Better Retrieval May Not Lead to Better Question Answering","date":"2022-05-07","arxiv_id":"2205.03685","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-copy-augmented-generative-model-for-open-1","title":"A Copy-Augmented Generative Model for Open-Domain Question Answering","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ed2lm-encoder-decoder-to-language-model-for-1","title":"ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference","date":"2022-04-25","arxiv_id":"2204.11458","repositories_listed":0,"syntology":null},{"url":null,"slug":"xlmrqa-open-domain-question-answering-on","title":"XLMRQA: Open-Domain Question Answering on Vietnamese Wikipedia-based Textual Knowledge Source","date":"2022-04-14","arxiv_id":"2204.07002","repositories_listed":0,"syntology":null},{"url":null,"slug":"augmenting-pre-trained-language-models-with","title":"Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering","date":"2022-04-10","arxiv_id":"2204.04581","repositories_listed":0,"syntology":null},{"url":null,"slug":"kgi-an-integrated-framework-for-knowledge","title":"KGI: An Integrated Framework for Knowledge Intensive Language Tasks","date":"2022-04-08","arxiv_id":"2204.03985","repositories_listed":0,"syntology":null},{"url":null,"slug":"multifaceted-improvements-for-conversational","title":"Multifaceted Improvements for Conversational Open-Domain Question Answering","date":"2022-04-01","arxiv_id":"2204.00266","repositories_listed":0,"syntology":null},{"url":null,"slug":"design-and-development-of-rule-based-open","title":"Design and Development of Rule-based open-domain Question-Answering System on SQuAD v2.0 Dataset","date":"2022-03-27","arxiv_id":"2204.09659","repositories_listed":0,"syntology":null},{"url":null,"slug":"targeted-extraction-of-temporal-facts-from","title":"Targeted Extraction of Temporal Facts from Textual Resources for Improved Temporal Question Answering over Knowledge Bases","date":"2022-03-21","arxiv_id":"2203.11054","repositories_listed":0,"syntology":null},{"url":null,"slug":"calibration-of-machine-reading-systems-at-1","title":"Calibration of Machine Reading Systems at Scale","date":"2022-03-20","arxiv_id":"2203.10623","repositories_listed":0,"syntology":null},{"url":null,"slug":"internet-augmented-language-models-through","title":"Internet-augmented language models through few-shot prompting for open-domain question answering","date":"2022-03-10","arxiv_id":"2203.05115","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-sentence-composition-reasoning-for","title":"Semantic Sentence Composition Reasoning for Multi-Hop Question Answering","date":"2022-03-01","arxiv_id":"2203.00160","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-open-domain-question-answering-systems","title":"Can Open Domain Question Answering Systems Answer Visual Knowledge Questions?","date":"2022-02-09","arxiv_id":"2202.04306","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-biomedical-information-retrieval","title":"Improving Biomedical Information Retrieval with Neural Retrievers","date":"2022-01-19","arxiv_id":"2201.07745","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-encoder-attribution-analysis-for-dense","title":"An Encoder Attribution Analysis for Dense Passage Retriever in Open-Domain Question Answering","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evidentiality-guided-generation-for-knowledge-1","title":"Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"in-situ-answer-sentence-selection-at-web","title":"In Situ Answer Sentence Selection at Web-scale","date":"2022-01-16","arxiv_id":"2201.05984","repositories_listed":0,"syntology":null},{"url":"/paper/reasoning-over-hybrid-chain-for-table-and-1","slug":"reasoning-over-hybrid-chain-for-table-and-1","title":"Reasoning over Hybrid Chain for Table-and-Text Open Domain QA","date":"2022-01-15","arxiv_id":"2201.05880","repositories_listed":0,"syntology":null},{"url":null,"slug":"scaling-up-query-focused-summarization-to","title":"Tackling Query-Focused Summarization as A Knowledge-Intensive Task: A Pilot Study","date":"2021-12-14","arxiv_id":"2112.07536","repositories_listed":0,"syntology":null},{"url":null,"slug":"you-only-need-one-model-for-open-domain","title":"You Only Need One Model for Open-domain Question Answering","date":"2021-12-14","arxiv_id":"2112.07381","repositories_listed":0,"syntology":null},{"url":null,"slug":"quality-and-cost-trade-offs-in-passage-re","title":"Quality and Cost Trade-offs in Passage Re-ranking Task","date":"2021-11-18","arxiv_id":"2111.09927","repositories_listed":0,"syntology":null},{"url":null,"slug":"archivalqa-a-large-scale-benchmark-dataset-1","title":"ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Archival News Collections","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"calibration-of-machine-reading-systems-at","title":"Calibration of Machine Reading Systems at Scale","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ccqa-a-new-web-scale-question-answering-1","title":"CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"get-your-model-puzzled-introducing-crossword","title":"Get Your Model Puzzled: Introducing Crossword-Solving as a New NLP Benchmark","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperlink-induced-pre-training-for-passage","title":"Hyperlink-induced Pre-training for Passage Retrieval of Open-domain Question Answering","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"open-domain-question-answering-over-virtual-1","title":"Open Domain Question Answering over Virtual Documents: A Unified Approach for Data and Text","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reasoning-over-hybrid-chain-for-table-and","title":"Reasoning over Hybrid Chain for Table-and-Text Open Domain Question Answering","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"salient-phrase-aware-dense-retrieval-can-a-1","title":"Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-open-domain-question-answering-1","title":"Unsupervised Open-Domain Question Answering with Higher Answerability","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-limited-labels-for-long-legal","title":"Learning from Limited Labels for Long Legal Dialogue","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"simple-and-effective-unsupervised-redundancy","title":"Simple and Effective Unsupervised Redundancy Elimination to Compress Dense Vectors for Passage Retrieval","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-copy-augmented-generative-model-for-open","title":"A Copy-Augmented Generative Model for Open-Domain Question Answering","date":"2021-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"c-more-pretraining-to-answer-open-domain","title":"C-MORE: Pretraining to Answer Open-Domain Questions by Consulting Millions of References","date":"2021-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"challenges-in-generalization-in-open-domain-1","title":"Challenges in Generalization in Open Domain Question Answering","date":"2021-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-lingual-genqa-a-language-agnostic","title":"Cross-Lingual Open-Domain Question Answering with Answer Sentence Generation","date":"2021-10-14","arxiv_id":"2110.07150","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-guided-generative-models-for","title":"Attention-guided Generative Models for Extractive Question Answering","date":"2021-10-12","arxiv_id":"2110.06393","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhance-long-text-understanding-via-distilled","title":"Enhance Long Text Understanding via Distilled Gist Detector from Abstractive Summarization","date":"2021-10-10","arxiv_id":"2110.04741","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-inductive-bias-of-in-context-learning-1","title":"The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design","date":"2021-10-09","arxiv_id":"2110.04541","repositories_listed":0,"syntology":null},{"url":null,"slug":"kg-fid-infusing-knowledge-graph-in-fusion-in-1","title":"KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering","date":"2021-10-08","arxiv_id":"2110.04330","repositories_listed":0,"syntology":null},{"url":null,"slug":"encoder-adaptation-of-dense-passage-retrieval","title":"Encoder Adaptation of Dense Passage Retrieval for Open-Domain Question Answering","date":"2021-10-04","arxiv_id":"2110.01599","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-universal-dense-retrieval-for-open","title":"Towards Universal Dense Retrieval for Open-domain Question Answering","date":"2021-09-23","arxiv_id":"2109.11085","repositories_listed":0,"syntology":null},{"url":null,"slug":"relation-guided-pre-training-for-open-domain","title":"Relation-Guided Pre-Training for Open-Domain Question Answering","date":"2021-09-21","arxiv_id":"2109.10346","repositories_listed":0,"syntology":null},{"url":null,"slug":"refocusing-on-relevance-personalization-in","title":"Refocusing on Relevance: Personalization in NLG","date":"2021-09-10","arxiv_id":"2109.05140","repositories_listed":0,"syntology":null},{"url":null,"slug":"archivalqa-a-large-scale-benchmark-dataset","title":"ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Historical News Collections","date":"2021-09-08","arxiv_id":"2109.03438","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoupled-transformer-for-scalable-inference-1","title":"Decoupled Transformer for Scalable Inference in Open-domain Question Answering","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-open-domain-question-answering","title":"Unsupervised Open-Domain Question Answering","date":"2021-08-31","arxiv_id":"2108.13817","repositories_listed":0,"syntology":null},{"url":"/paper/0-8-nyquist-computational-ghost-imaging-via","slug":"0-8-nyquist-computational-ghost-imaging-via","title":"0.8% Nyquist computational ghost imaging via non-experimental deep learning","date":"2021-08-17","arxiv_id":"2108.07673","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoupled-transformer-for-scalable-inference","title":"Decoupled Transformer for Scalable Inference in Open-domain Question Answering","date":"2021-08-05","arxiv_id":"2108.02765","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-open-domain-question-answering-with","title":"Combining Open Domain Question Answering with a Task-Oriented Dialog System","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"xmoco-cross-momentum-contrastive-learning-for","title":"xMoCo: Cross Momentum Contrastive Learning for Open-Domain Question Answering","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-claim-review-for-climate-science","title":"Automatic Claim Review for Climate Science via Explanation Generation","date":"2021-07-30","arxiv_id":"2107.14740","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-knowledge-based-approach-for-answering","title":"A Knowledge-based Approach for Answering Complex Questions in Persian","date":"2021-07-05","arxiv_id":"2107.02040","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-we-there-yet-exploring-clinical-domain","title":"Are we there yet? Exploring clinical domain knowledge of BERT models","date":"2021-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reconsider-improved-re-ranking-using-span","title":"RECONSIDER: Improved Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering","date":"2021-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"geographic-question-answering-challenges","title":"Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions","date":"2021-05-19","arxiv_id":"2105.09392","repositories_listed":0,"syntology":null},{"url":null,"slug":"unik-qa-unified-representations-of-structured","title":"UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering","date":"2021-05-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-retrieval-optimized-multi-task","title":"Efficient Retrieval Optimized Multi-task Learning","date":"2021-04-20","arxiv_id":"2104.10129","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextualized-query-embeddings-for","title":"Contextualized Query Embeddings for Conversational Search","date":"2021-04-18","arxiv_id":"2104.08707","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-graph-guided-multi-round-retrieval-method","title":"A Graph-guided Multi-round Retrieval Method for Conversational Open-domain Question Answering","date":"2021-04-17","arxiv_id":"2104.08443","repositories_listed":0,"syntology":null},{"url":null,"slug":"complementary-evidence-identification-in-open","title":"Complementary Evidence Identification in Open-Domain Question Answering","date":"2021-03-22","arxiv_id":"2103.11643","repositories_listed":0,"syntology":null},{"url":null,"slug":"reasoning-over-virtual-knowledge-bases-with","title":"Reasoning Over Virtual Knowledge Bases With Open Predicate Relations","date":"2021-02-14","arxiv_id":"2102.07043","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-context-in-answer-sentence-selection","title":"Modeling Context in Answer Sentence Selection Systems on a Latency Budget","date":"2021-01-28","arxiv_id":"2101.12093","repositories_listed":0,"syntology":null},{"url":"/paper/efficientqa-a-roberta-based-phrase-indexed","slug":"efficientqa-a-roberta-based-phrase-indexed","title":"EfficientQA : a RoBERTa Based Phrase-Indexed Question-Answering System","date":"2021-01-06","arxiv_id":"2101.02157","repositories_listed":0,"syntology":null}],"record_sha256":"868e44ba88014798848077fa1b4bd705e5a908c4243582b66d89fe421e15f178","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}