{"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/question-answering/papers/74","list_of":"/task/question-answering","task":"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":74,"pages_in_order":109,"rows_per_page":100,"rows":[7301,7400],"of":10817,"counts":{"archive_papers_tagged":10817,"with_a_code_link":4171,"where_syntology_ran_a_sample":1274,"not_listed_spam_title":0,"listed":10817,"listed_where_code_ran":1274,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1073,"every_run_a_failure_of_syntologys_instrument":201,"listed_with_a_run_with_no_instrument_failure":1073,"listed_every_run_a_failure_of_syntologys_instrument":201,"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/question-answering","prev":"/task/question-answering/papers/73","next":"/task/question-answering/papers/75","papers":[{"url":null,"slug":"improved-and-efficient-conversational-slot-1","title":"Improved and Efficient Conversational Slot Labeling through Question Answering","date":"2022-04-05","arxiv_id":"2204.02123","repositories_listed":0,"syntology":null},{"url":"/paper/multi-view-approach-to-suggest-moderation","slug":"multi-view-approach-to-suggest-moderation","title":"Multi-View Approach to Suggest Moderation Actions in Community Question Answering Sites","date":"2022-04-04","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"question-driven-graph-fusion-network-for","title":"Question-Driven Graph Fusion Network For Visual Question Answering","date":"2022-04-03","arxiv_id":"2204.00975","repositories_listed":0,"syntology":null},{"url":null,"slug":"co-vqa-answering-by-interactive-sub-question-1","title":"Co-VQA : Answering by Interactive Sub Question Sequence","date":"2022-04-02","arxiv_id":"2204.00879","repositories_listed":0,"syntology":null},{"url":null,"slug":"cool-a-context-outlooker-and-its-application","title":"COOL, a Context Outlooker, and its Application to Question Answering and other Natural Language Processing Tasks","date":"2022-04-01","arxiv_id":"2204.09593","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":"syntax-informed-question-answering-with","title":"Syntax-informed Question Answering with Heterogeneous Graph Transformer","date":"2022-04-01","arxiv_id":"2204.09655","repositories_listed":0,"syntology":null},{"url":null,"slug":"simvqa-exploring-simulated-environments-for","title":"SimVQA: Exploring Simulated Environments for Visual Question Answering","date":"2022-03-31","arxiv_id":"2203.17219","repositories_listed":0,"syntology":null},{"url":null,"slug":"auto-mlm-improved-contrastive-learning-for","title":"Auto-MLM: Improved Contrastive Learning for Self-supervised Multi-lingual Knowledge Retrieval","date":"2022-03-30","arxiv_id":"2203.16187","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-table-question-answering-via","title":"End-to-End Table Question Answering via Retrieval-Augmented Generation","date":"2022-03-30","arxiv_id":"2203.16714","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-differential-relational-privacy-and","title":"Towards Differential Relational Privacy and its use in Question Answering","date":"2022-03-30","arxiv_id":"2203.16701","repositories_listed":0,"syntology":null},{"url":null,"slug":"anna-enhanced-language-representation-for-1","title":"ANNA: Enhanced Language Representation for Question Answering","date":"2022-03-28","arxiv_id":"2203.14507","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":"improving-question-answering-over-knowledge","title":"Improving Question Answering over Knowledge Graphs Using Graph Summarization","date":"2022-03-25","arxiv_id":"2203.13570","repositories_listed":0,"syntology":null},{"url":null,"slug":"gender-and-racial-stereotype-detection-in","title":"Gender and Racial Stereotype Detection in Legal Opinion Word Embeddings","date":"2022-03-24","arxiv_id":"2203.13369","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-escaping-from-language-bias-and-ocr","title":"Towards Escaping from Language Bias and OCR Error: Semantics-Centered Text Visual Question Answering","date":"2022-03-24","arxiv_id":"2203.12929","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-theoretically-grounded-benchmark-for","title":"A Theoretically Grounded Benchmark for Evaluating Machine Commonsense","date":"2022-03-23","arxiv_id":"2203.12184","repositories_listed":0,"syntology":null},{"url":null,"slug":"vlsp-2021-shared-task-vietnamese-machine","title":"VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension","date":"2022-03-22","arxiv_id":"2203.11400","repositories_listed":0,"syntology":null},{"url":null,"slug":"wudaomm-a-large-scale-multi-modal-dataset-for","title":"WuDaoMM: A large-scale Multi-Modal Dataset for Pre-training models","date":"2022-03-22","arxiv_id":"2203.11480","repositories_listed":0,"syntology":null},{"url":null,"slug":"programming-language-agnostic-mining-of-code","title":"Programming Language Agnostic Mining of Code and Language Pairs with Sequence Labeling Based Question Answering","date":"2022-03-21","arxiv_id":"2203.10744","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":"are-you-robert-or-roberta-deceiving-online","title":"Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators","date":"2022-03-18","arxiv_id":"2203.09813","repositories_listed":0,"syntology":null},{"url":null,"slug":"ask-to-understand-question-generation-for","title":"Ask to Understand: Question Generation for Multi-hop Question Answering","date":"2022-03-17","arxiv_id":"2203.09073","repositories_listed":0,"syntology":null},{"url":null,"slug":"dp-kb-data-programming-with-knowledge-bases-1","title":"DP-KB: Data Programming with Knowledge Bases Improves Transformer Fine Tuning for Answer Sentence Selection","date":"2022-03-17","arxiv_id":"2203.09598","repositories_listed":0,"syntology":null},{"url":null,"slug":"elberto-self-supervised-commonsense-learning","title":"elBERto: Self-supervised Commonsense Learning for Question Answering","date":"2022-03-17","arxiv_id":"2203.09424","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-quantum-density-matrix-in","title":"A New Quantum CNN Model for Image Classification","date":"2022-03-16","arxiv_id":"2203.11155","repositories_listed":0,"syntology":null},{"url":"/paper/e-kar-a-benchmark-for-rationalizing-natural-1","slug":"e-kar-a-benchmark-for-rationalizing-natural-1","title":"E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning","date":"2022-03-16","arxiv_id":"2203.08480","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-you-even-tell-left-from-right-presenting","title":"Can you even tell left from right? Presenting a new challenge for VQA","date":"2022-03-15","arxiv_id":"2203.07664","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-but-not-robust-comparing-the","title":"Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness","date":"2022-03-15","arxiv_id":"2203.07653","repositories_listed":0,"syntology":null},{"url":null,"slug":"choose-your-qa-model-wisely-a-systematic","title":"Choose Your QA Model Wisely: A Systematic Study of Generative and Extractive Readers for Question Answering","date":"2022-03-14","arxiv_id":"2203.07522","repositories_listed":0,"syntology":null},{"url":null,"slug":"clip-models-are-few-shot-learners-empirical","title":"CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment","date":"2022-03-14","arxiv_id":"2203.07190","repositories_listed":0,"syntology":null},{"url":"/paper/efficient-language-modeling-with-sparse-all","slug":"efficient-language-modeling-with-sparse-all","title":"Efficient Language Modeling with Sparse all-MLP","date":"2022-03-14","arxiv_id":"2203.06850","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-semantic-search-for-community","title":"Towards Semantic Search for Community Question Answering for Mortgage Officers","date":"2022-03-14","arxiv_id":"2203.06807","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-visual-prompt-temporal-answering","title":"Towards Visual-Prompt Temporal Answering Grounding in Medical Instructional Video","date":"2022-03-13","arxiv_id":"2203.06667","repositories_listed":0,"syntology":null},{"url":null,"slug":"enabling-multimodal-generation-on-clip-via-1","title":"Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation","date":"2022-03-12","arxiv_id":"2203.06386","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":"language-model-driven-negative-sampling","title":"LEMON: LanguagE ModeL for Negative Sampling of Knowledge Graph Embeddings","date":"2022-03-09","arxiv_id":"2203.04703","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-coreference-relations-in-visual-1","title":"Modeling Coreference Relations in Visual Dialog","date":"2022-03-06","arxiv_id":"2203.02986","repositories_listed":0,"syntology":null},{"url":null,"slug":"recent-rapid-advancement-in-visual-question","title":"Recent, rapid advancement in visual question answering architecture: a review","date":"2022-03-02","arxiv_id":"2203.01322","repositories_listed":0,"syntology":null},{"url":"/paper/improving-time-sensitivity-for-question","slug":"improving-time-sensitivity-for-question","title":"Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs","date":"2022-03-01","arxiv_id":"2203.00255","repositories_listed":0,"syntology":null},{"url":"/paper/read-before-generate-faithful-long-form","slug":"read-before-generate-faithful-long-form","title":"Read before Generate! Faithful Long Form Question Answering with Machine Reading","date":"2022-03-01","arxiv_id":"2203.00343","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":"improving-lexical-embeddings-for-robust","title":"Improving Lexical Embeddings for Robust Question Answering","date":"2022-02-28","arxiv_id":"2202.13636","repositories_listed":0,"syntology":null},{"url":null,"slug":"tis-but-thy-name-semantic-question-answering","title":"'Tis but Thy Name: Semantic Question Answering Evaluation with 11M Names for 1M Entities","date":"2022-02-28","arxiv_id":"2202.13581","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generative-model-for-relation-extraction","title":"A Generative Model for Relation Extraction and Classification","date":"2022-02-26","arxiv_id":"2202.13229","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-clevrness-blackbox-testing-of","title":"Measuring CLEVRness: Blackbox testing of Visual Reasoning Models","date":"2022-02-24","arxiv_id":"2202.12162","repositories_listed":0,"syntology":null},{"url":"/paper/2-5-1-d-spatio-temporal-scene-graphs-for","slug":"2-5-1-d-spatio-temporal-scene-graphs-for","title":"(2.5+1)D Spatio-Temporal Scene Graphs for Video Question Answering","date":"2022-02-18","arxiv_id":"2202.09277","repositories_listed":0,"syntology":null},{"url":null,"slug":"discovering-fine-grained-semantics-in","title":"Discovering Fine-Grained Semantics in Knowledge Graph Relations","date":"2022-02-17","arxiv_id":"2202.08917","repositories_listed":0,"syntology":null},{"url":null,"slug":"question-answer-sentence-graph-for-joint","title":"Question-Answer Sentence Graph for Joint Modeling Answer Selection","date":"2022-02-16","arxiv_id":"2203.03549","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-visual-question-answering","title":"Privacy Preserving Visual Question Answering","date":"2022-02-15","arxiv_id":"2202.07712","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-experimental-study-of-the-vision","title":"An experimental study of the vision-bottleneck in VQA","date":"2022-02-14","arxiv_id":"2202.06858","repositories_listed":0,"syntology":null},{"url":null,"slug":"partially-fake-audio-detection-by-self","title":"Partially Fake Audio Detection by Self-attention-based Fake Span Discovery","date":"2022-02-14","arxiv_id":"2202.06684","repositories_listed":0,"syntology":null},{"url":"/paper/pquad-a-persian-question-answering-dataset","slug":"pquad-a-persian-question-answering-dataset","title":"PQuAD: A Persian Question Answering Dataset","date":"2022-02-13","arxiv_id":"2202.06219","repositories_listed":0,"syntology":null},{"url":null,"slug":"recognition-free-question-answering-on","title":"Recognition-free Question Answering on Handwritten Document Collections","date":"2022-02-12","arxiv_id":"2202.06080","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":"/paper/newskvqa-knowledge-aware-news-video-question","slug":"newskvqa-knowledge-aware-news-video-question","title":"NEWSKVQA: Knowledge-Aware News Video Question Answering","date":"2022-02-08","arxiv_id":"2202.04015","repositories_listed":0,"syntology":null},{"url":null,"slug":"rnn-transducers-for-nested-named-entity","title":"RNN Transducers for Nested Named Entity Recognition with constraints on alignment for long sequences","date":"2022-02-08","arxiv_id":"2203.03543","repositories_listed":0,"syntology":null},{"url":null,"slug":"survey-of-hallucination-in-natural-language","title":"Survey of Hallucination in Natural Language Generation","date":"2022-02-08","arxiv_id":"2202.03629","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-loosely-coupling-knowledge-graph","title":"Towards Loosely-Coupling Knowledge Graph Embeddings and Ontology-based Reasoning","date":"2022-02-07","arxiv_id":"2202.03173","repositories_listed":0,"syntology":null},{"url":"/paper/gatortron-a-large-clinical-language-model-to","slug":"gatortron-a-large-clinical-language-model-to","title":"GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records","date":"2022-02-02","arxiv_id":"2203.03540","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-over-multiple-domains-in-1","title":"Active Learning Over Multiple Domains in Natural Language Tasks","date":"2022-02-01","arxiv_id":"2202.00254","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-annotation-and-querying-framework","title":"Semantic Annotation and Querying Framework based on Semi-structured Ayurvedic Text","date":"2022-02-01","arxiv_id":"2202.00216","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-representations-of-entities-and","title":"Learning Representations of Entities and Relations","date":"2022-01-31","arxiv_id":"2201.13073","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-automated-question-answering-framework","title":"An Automated Question-Answering Framework Based on Evolution Algorithm","date":"2022-01-26","arxiv_id":"2201.10797","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-compose-diversified-prompts-for","title":"Learning to Compose Diversified Prompts for Image Emotion Classification","date":"2022-01-26","arxiv_id":"2201.10963","repositories_listed":0,"syntology":null},{"url":null,"slug":"mga-vqa-multi-granularity-alignment-for-1","title":"MGA-VQA: Multi-Granularity Alignment for Visual Question Answering","date":"2022-01-25","arxiv_id":"2201.10656","repositories_listed":0,"syntology":null},{"url":null,"slug":"sa-vqa-structured-alignment-of-visual-and","title":"SA-VQA: Structured Alignment of Visual and Semantic Representations for Visual Question Answering","date":"2022-01-25","arxiv_id":"2201.10654","repositories_listed":0,"syntology":null},{"url":null,"slug":"artefact-retrieval-overview-of-nlp-models-1","title":"Artefact Retrieval: Overview of NLP Models with Knowledge Base Access","date":"2022-01-24","arxiv_id":"2201.09651","repositories_listed":0,"syntology":null},{"url":null,"slug":"question-generation-for-evaluating-cross","title":"Question Generation for Evaluating Cross-Dataset Shifts in Multi-modal Grounding","date":"2022-01-24","arxiv_id":"2201.09639","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-collaborative-question-answering-a","title":"Towards Collaborative Question Answering: A Preliminary Study","date":"2022-01-24","arxiv_id":"2201.09708","repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-question-generation-with-continual","title":"Unified Question Generation with Continual Lifelong Learning","date":"2022-01-24","arxiv_id":"2201.09696","repositories_listed":0,"syntology":null},{"url":null,"slug":"conversational-information-seeking","title":"Conversational Information Seeking","date":"2022-01-21","arxiv_id":"2201.08808","repositories_listed":0,"syntology":null},{"url":null,"slug":"astbert-enabling-language-model-for-code","title":"AstBERT: Enabling Language Model for Financial Code Understanding with Abstract Syntax Trees","date":"2022-01-20","arxiv_id":"2201.07984","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-knowledge-graphs-using-typed","title":"Enhanced Knowledge Graphs Using Typed Entailment Graphs","date":"2022-01-20","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-machine-common-sense-via-cloze","title":"Evaluating Machine Common Sense via Cloze Testing","date":"2022-01-19","arxiv_id":"2201.07902","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":"do-kg-augmented-models-leverage-knowledge-as","title":"Do KG-augmented Models Leverage Knowledge as Humans Do?","date":"2022-01-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generalizable-neuro-symbolic-systems-for","title":"Generalizable Neuro-symbolic Systems for Commonsense Question Answering","date":"2022-01-17","arxiv_id":"2201.06230","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-graph-papers-iclr-2021","title":"Knowledge Graph Papers @ ICLR 2021","date":"2022-01-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/a-two-stage-approach-towards-generalization-1","slug":"a-two-stage-approach-towards-generalization-1","title":"A Two-Stage Approach towards Generalization in Knowledge Base Question Answering","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"all-you-may-need-for-vqa-are-image-captions","title":"All You May Need for VQA are Image Captions","date":"2022-01-16","arxiv_id":null,"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":"bayesian-deep-learning-for-interactive","title":"Bayesian Deep Learning for Interactive Community Question Answering","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"breaking-character-are-subwords-good-enough","title":"Breaking Character: Are Subwords Good Enough for MRLs After All?","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-distillation-for-language-models-1","title":"Causal Distillation for Language Models","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cl-rekd-cross-lingual-knowledge-distillation","title":"CL-ReKD: Cross-lingual Knowledge Distillation for Multilingual Retrieval Question Answering","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"conqrr-conversational-query-rewriting-for-1","title":"CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"consecutive-question-generation-with","title":"Consecutive Question Generation with Multitask Joint Reranking and Dynamic Rationale Search","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"context-guided-triple-matching-for-multiple-1","title":"Context-guided Triple Matching for Multiple Choice Question Answering","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cooperative-self-training-of-machine-reading","title":"Cooperative Self-training of Machine Reading Comprehension","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-for-biomedical-factoid","title":"Data Augmentation for Biomedical Factoid Question Answering","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"disaggregating-hops-can-we-guide-a-multi-hop","title":"Disaggregating Hops: Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at each Hop?","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"divide-and-conquer-text-semantic-matching","title":"Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"double-retrieval-and-ranking-for-accurate","title":"Double Retrieval and Ranking for Accurate Question Answering","date":"2022-01-16","arxiv_id":"2201.05981","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-textless-spoken-question-answering-with","title":"DUAL: Textless Spoken Question Answering with Speech Discrete Unit Adaptive Learning","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-relevance-graph-network-for-knowledge","title":"Dynamic Relevance Graph Network for Knowledge-Aware 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":"faq-search-using-transformers","title":"FAQ Search using Transformers","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fednlp-benchmarking-federated-learning","title":"FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"0f3ab31171d70cff61b00b8958362b37d5d971b062fd4d40341e14b9eb575766","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}