{"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/81","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":81,"pages_in_order":109,"rows_per_page":100,"rows":[8001,8100],"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/80","next":"/task/question-answering/papers/82","papers":[{"url":null,"slug":"conversational-answer-generation-and","title":"Conversational Answer Generation and Factuality for Reading Comprehension Question-Answering","date":"2021-03-11","arxiv_id":"2103.06500","repositories_listed":0,"syntology":null},{"url":null,"slug":"rl-csdia-representation-learning-of-computer","title":"RL-CSDia: Representation Learning of Computer Science Diagrams","date":"2021-03-10","arxiv_id":"2103.05900","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-embodied-ai-from-simulator-to","title":"A Survey of Embodied AI: From Simulators to Research Tasks","date":"2021-03-08","arxiv_id":"2103.04918","repositories_listed":0,"syntology":null},{"url":null,"slug":"mcr-net-a-multi-step-co-interactive-relation","title":"MCR-Net: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehension","date":"2021-03-08","arxiv_id":"2103.04567","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-based-tri-attention-network-for-answer","title":"Graph-Based Tri-Attention Network for Answer Ranking in CQA","date":"2021-03-05","arxiv_id":"2103.03583","repositories_listed":0,"syntology":null},{"url":null,"slug":"advances-in-multi-turn-dialogue-comprehension","title":"Advances in Multi-turn Dialogue Comprehension: A Survey","date":"2021-03-04","arxiv_id":"2103.03125","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-linguistic-study-on-relevance-modeling-in","title":"A Linguistic Study on Relevance Modeling in Information Retrieval","date":"2021-03-01","arxiv_id":"2103.00956","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-reasoning-paths-over-semantic-graphs-1","title":"Learning Reasoning Paths over Semantic Graphs for Video-grounded Dialogues","date":"2021-03-01","arxiv_id":"2103.00820","repositories_listed":0,"syntology":null},{"url":null,"slug":"docent-learning-self-supervised-entity","title":"DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections","date":"2021-02-26","arxiv_id":"2102.13247","repositories_listed":0,"syntology":null},{"url":null,"slug":"natural-language-video-localization-a-revisit","title":"Natural Language Video Localization: A Revisit in Span-based Question Answering Framework","date":"2021-02-26","arxiv_id":"2102.13558","repositories_listed":0,"syntology":null},{"url":null,"slug":"iie-nlp-eyas-at-semeval-2021-task-4-enhancing","title":"IIE-NLP-Eyas at SemEval-2021 Task 4: Enhancing PLM for ReCAM with Special Tokens, Re-Ranking, Siamese Encoders and Back Translation","date":"2021-02-25","arxiv_id":"2102.12777","repositories_listed":0,"syntology":null},{"url":null,"slug":"pharmke-knowledge-extraction-platform-for","title":"PharmKE: Knowledge Extraction Platform for Pharmaceutical Texts using Transfer Learning","date":"2021-02-25","arxiv_id":"2102.13139","repositories_listed":0,"syntology":null},{"url":null,"slug":"multichannel-lstm-cnn-for-telugu-technical","title":"Multichannel LSTM-CNN for Telugu Technical Domain Identification","date":"2021-02-24","arxiv_id":"2102.12179","repositories_listed":0,"syntology":null},{"url":null,"slug":"onestop-qamaker-extract-question-answer-pairs","title":"OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach","date":"2021-02-24","arxiv_id":"2102.12128","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-compositional-representation-for-few","title":"Learning Compositional Representation for Few-shot Visual Question Answering","date":"2021-02-21","arxiv_id":"2102.10575","repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-answer-sentence-reranking-via","title":"Multilingual Answer Sentence Reranking via Automatically Translated Data","date":"2021-02-20","arxiv_id":"2102.10250","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-low-resource-biomedical-qa-via","title":"Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies","date":"2021-02-16","arxiv_id":"2102.08366","repositories_listed":0,"syntology":null},{"url":null,"slug":"user-inspired-posterior-network-for","title":"User-Inspired Posterior Network for Recommendation Reason Generation","date":"2021-02-16","arxiv_id":"2102.07919","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledgecheckr-intelligent-techniques-for","title":"KnowledgeCheckR: Intelligent Techniques for Counteracting Forgetting","date":"2021-02-15","arxiv_id":"2102.07825","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":"/paper/a-large-batch-optimizer-reality-check","slug":"a-large-batch-optimizer-reality-check","title":"A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes","date":"2021-02-12","arxiv_id":"2102.06356","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-inference-performance-of","title":"Optimizing Inference Performance of Transformers on CPUs","date":"2021-02-12","arxiv_id":"2102.06621","repositories_listed":0,"syntology":null},{"url":null,"slug":"biomedical-question-answering-a-comprehensive","title":"Biomedical Question Answering: A Survey of Approaches and Challenges","date":"2021-02-10","arxiv_id":"2102.05281","repositories_listed":0,"syntology":null},{"url":null,"slug":"information-extraction-from-co-occurring","title":"Information Extraction From Co-Occurring Similar Entities","date":"2021-02-10","arxiv_id":"2102.05444","repositories_listed":0,"syntology":null},{"url":null,"slug":"decontextualization-making-sentences-stand","title":"Decontextualization: Making Sentences Stand-Alone","date":"2021-02-09","arxiv_id":"2102.05169","repositories_listed":0,"syntology":null},{"url":null,"slug":"memory-augmented-sequential-paragraph","title":"Memory Augmented Sequential Paragraph Retrieval for Multi-hop Question Answering","date":"2021-02-07","arxiv_id":"2102.03741","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-agnostic-answer-reranking-system-for","title":"Model Agnostic Answer Reranking System for Adversarial Question Answering","date":"2021-02-05","arxiv_id":"2102.03016","repositories_listed":0,"syntology":null},{"url":"/paper/think-you-have-solved-direct-answer-question","slug":"think-you-have-solved-direct-answer-question","title":"Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challenge","date":"2021-02-05","arxiv_id":"2102.03315","repositories_listed":0,"syntology":null},{"url":"/paper/the-gem-benchmark-natural-language-generation","slug":"the-gem-benchmark-natural-language-generation","title":"The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics","date":"2021-02-02","arxiv_id":"2102.01672","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-small-and-synthetic-benchmarks-drive","title":"Do Question Answering Modeling Improvements Hold Across Benchmarks?","date":"2021-02-01","arxiv_id":"2102.01065","repositories_listed":0,"syntology":null},{"url":null,"slug":"revamp-enhancing-accessible-information","title":"Revamp: Enhancing Accessible Information Seeking Experience of Online Shopping for Blind or Low Vision Users","date":"2021-02-01","arxiv_id":"2102.00576","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-teaching-machines-to-read-and-comprehend","title":"Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data","date":"2021-02-01","arxiv_id":"2102.01226","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-study-on-the-generalization","title":"An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games","date":"2021-01-31","arxiv_id":"2102.00424","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":null,"slug":"vx2text-end-to-end-learning-of-video-based","title":"VX2TEXT: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs","date":"2021-01-28","arxiv_id":"2101.12059","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-neuro-symbolic-module-1","title":"Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning","date":"2021-01-28","arxiv_id":"2101.11802","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-driven-natural-language","title":"Knowledge-driven Natural Language Understanding of English Text and its Applications","date":"2021-01-27","arxiv_id":"2101.11707","repositories_listed":0,"syntology":null},{"url":null,"slug":"powering-covid-19-community-q-a-with-curated","title":"Powering COVID-19 community Q&A with Curated Side Information","date":"2021-01-27","arxiv_id":"2101.11556","repositories_listed":0,"syntology":null},{"url":null,"slug":"representations-for-question-answering-from","title":"Representations for Question Answering from Documents with Tables and Text","date":"2021-01-26","arxiv_id":"2101.10573","repositories_listed":0,"syntology":null},{"url":null,"slug":"unanswerable-questions-about-images-and-texts","title":"Unanswerable Questions about Images and Texts","date":"2021-01-25","arxiv_id":"2102.06793","repositories_listed":0,"syntology":null},{"url":null,"slug":"a2p-mann-adaptive-attention-inference-hops","title":"A2P-MANN: Adaptive Attention Inference Hops Pruned Memory-Augmented Neural Networks","date":"2021-01-24","arxiv_id":"2101.09693","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-natural-language-question-answering","title":"Towards Natural Language Question Answering over Earth Observation Linked Data using Attention-based Neural Machine Translation","date":"2021-01-23","arxiv_id":"2101.09427","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-question-answering-based-on-local","title":"Visual Question Answering based on Local-Scene-Aware Referring Expression Generation","date":"2021-01-22","arxiv_id":"2101.08978","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-clustering-of-short-text-streams-using","title":"Fast Clustering of Short Text Streams Using Efficient Cluster Indexing and Dynamic Similarity Thresholds","date":"2021-01-21","arxiv_id":"2101.08595","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-confident-machine-reading","title":"Towards Confident Machine Reading Comprehension","date":"2021-01-20","arxiv_id":"2101.07942","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-generalization-in-dialog-state","title":"Zero-shot Generalization in Dialog State Tracking through Generative Question Answering","date":"2021-01-20","arxiv_id":"2101.08333","repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-knowledge-based-question","title":"Incremental Knowledge Based Question Answering","date":"2021-01-18","arxiv_id":"2101.06938","repositories_listed":0,"syntology":null},{"url":null,"slug":"tip-of-the-tongue-known-item-retrieval-a-case","title":"Tip of the Tongue Known-Item Retrieval: A Case Study in Movie Identification","date":"2021-01-18","arxiv_id":"2101.07124","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyster-a-hybrid-spatio-temporal-event","title":"HySTER: A Hybrid Spatio-Temporal Event Reasoner","date":"2021-01-17","arxiv_id":"2101.06644","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-in-artificial-intelligence","title":"Understanding in Artificial Intelligence","date":"2021-01-17","arxiv_id":"2101.06573","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-variable-models-for-visual-question","title":"Latent Variable Models for Visual Question Answering","date":"2021-01-16","arxiv_id":"2101.06399","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-based-models-for-question","title":"Transformer-Based Models for Question Answering on COVID19","date":"2021-01-16","arxiv_id":"2101.11432","repositories_listed":0,"syntology":null},{"url":null,"slug":"coarse-grained-decomposition-and-fine-grained","title":"Coarse-grained decomposition and fine-grained interaction for multi-hop question answering","date":"2021-01-15","arxiv_id":"2101.05988","repositories_listed":0,"syntology":null},{"url":null,"slug":"grid-search-hyperparameter-benchmarking-of","title":"Grid Search Hyperparameter Benchmarking of BERT, ALBERT, and LongFormer on DuoRC","date":"2021-01-15","arxiv_id":"2101.06326","repositories_listed":0,"syntology":null},{"url":null,"slug":"reasoning-over-vision-and-language-exploring","title":"Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge","date":"2021-01-15","arxiv_id":"2101.06013","repositories_listed":0,"syntology":null},{"url":null,"slug":"recent-advances-in-video-question-answering-a","title":"Recent Advances in Video Question Answering: A Review of Datasets and Methods","date":"2021-01-15","arxiv_id":"2101.05954","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-the-role-of-scene-graphs-in","title":"Understanding the Role of Scene Graphs in Visual Question Answering","date":"2021-01-14","arxiv_id":"2101.05479","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-relative-depth-between-objects","title":"Predicting Relative Depth between Objects from Semantic Features","date":"2021-01-12","arxiv_id":"2101.04626","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-neural-question-answering-system-for-basic","title":"A Neural Question Answering System for Basic Questions about Subroutines","date":"2021-01-11","arxiv_id":"2101.03999","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-applications-for-covid-19","title":"Deep Learning applications for COVID-19","date":"2021-01-11","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-better-sentence-representation-with","title":"Learning Better Sentence Representation with Syntax Information","date":"2021-01-09","arxiv_id":"2101.03343","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-word-sense-disambiguation-approach","title":"A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph","date":"2021-01-08","arxiv_id":"2101.02875","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},{"url":null,"slug":"modeling-global-semantics-for-question","title":"Modeling Global Semantics for Question Answering over Knowledge Bases","date":"2021-01-05","arxiv_id":"2101.01510","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarking-knowledge-enhanced-commonsense","title":"Benchmarking Knowledge-Enhanced Commonsense Question Answering via Knowledge-to-Text Transformation","date":"2021-01-04","arxiv_id":"2101.00760","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieving-and-reading-a-comprehensive-survey","title":"Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering","date":"2021-01-04","arxiv_id":"2101.00774","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformers-in-vision-a-survey","title":"Transformers in Vision: A Survey","date":"2021-01-04","arxiv_id":"2101.01169","repositories_listed":0,"syntology":null},{"url":"/paper/riddlesense-answering-riddle-questions-as","slug":"riddlesense-answering-riddle-questions-as","title":"RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge","date":"2021-01-02","arxiv_id":"2101.00376","repositories_listed":0,"syntology":null},{"url":null,"slug":"which-linguist-invented-the-lightbulb","title":"Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering","date":"2021-01-02","arxiv_id":"2101.00391","repositories_listed":0,"syntology":null},{"url":null,"slug":"block-skim-transformer-for-efficient-question","title":"Block Skim Transformer for Efficient Question Answering","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"chemistryqa-a-complex-question-answering","title":"ChemistryQA: A Complex Question Answering Dataset from Chemistry","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cluster-former-clustering-based-sparse-1","title":"Cluster-Former: Clustering-based Sparse Transformer for Question Answering","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"differentiable-end-to-end-program-executor","title":"Differentiable End-to-End Program Executor for Sample and Computationally Efficient VQA","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"env-qa-a-video-question-answering-benchmark","title":"Env-QA: A Video Question Answering Benchmark for Comprehensive Understanding of Dynamic Environments","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"erasure-for-advancing-dynamic-self-supervised","title":"Erasure for Advancing: Dynamic Self-Supervised Learning for Commonsense Reasoning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hair-hierarchical-visual-semantic-relational","title":"HAIR: Hierarchical Visual-Semantic Relational Reasoning for Video Question Answering","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-graph-attention-network-for-few","title":"Hierarchical Graph Attention Network for Few-Shot Visual-Semantic Learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-contextualized-knowledge-graph","title":"Learning Contextualized Knowledge Graph Structures for Commonsense Reasoning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/learning-to-generate-questions-by-recovering","slug":"learning-to-generate-questions-by-recovering","title":"Learning to Generate Questions by Recovering Answer-containing Sentences","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"linguistically-routing-capsule-network-for","title":"Linguistically Routing Capsule Network for Out-of-Distribution Visual Question Answering","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"memory-representation-in-transformer","title":"Memory Representation in Transformer","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-span-question-answering-using-span","title":"MULTI-SPAN QUESTION ANSWERING USING SPAN-IMAGE NETWORK","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-retrieval-for-knowledge-intensive","title":"Multi-task Retrieval for Knowledge-Intensive Tasks","date":"2021-01-01","arxiv_id":"2101.00117","repositories_listed":0,"syntology":null},{"url":null,"slug":"neurips-2020-efficientqa-competition-systems","title":"NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned","date":"2021-01-01","arxiv_id":"2101.00133","repositories_listed":0,"syntology":null},{"url":null,"slug":"pabi-a-unified-pac-bayesian-informativeness","title":"PABI: A Unified PAC-Bayesian Informativeness Measure for Incidental Supervision Signals","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-the-impact-of-dataset-composition","title":"Predicting the impact of dataset composition on model performance","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pretrain-knowledge-aware-language-models","title":"Pretrain Knowledge-Aware Language Models","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"symbol-shift-equivariant-neural-networks","title":"Symbol-Shift Equivariant Neural Networks","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/unitedqa-a-hybrid-approach-for-open-domain","slug":"unitedqa-a-hybrid-approach-for-open-domain","title":"UnitedQA: A Hybrid Approach for Open Domain Question Answering","date":"2021-01-01","arxiv_id":"2101.00178","repositories_listed":0,"syntology":null},{"url":null,"slug":"unshuffling-data-for-improved-generalization-1","title":"Unshuffling Data for Improved Generalization in Visual Question Answering","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"video-question-answering-using-language","title":"Video Question Answering Using Language-Guided Deep Compressed-Domain Video Feature","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"vilnmn-a-neural-module-network-approach-to","title":"VilNMN: A Neural Module Network approach to Video-Grounded Language Tasks","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/clear-contrastive-learning-for-sentence","slug":"clear-contrastive-learning-for-sentence","title":"CLEAR: Contrastive Learning for Sentence Representation","date":"2020-12-31","arxiv_id":"2012.15466","repositories_listed":0,"syntology":null},{"url":null,"slug":"fid-ex-improving-sequence-to-sequence-models","title":"FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation","date":"2020-12-31","arxiv_id":"2012.15482","repositories_listed":0,"syntology":null},{"url":"/paper/hopretriever-retrieve-hops-over-wikipedia-to","slug":"hopretriever-retrieve-hops-over-wikipedia-to","title":"HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions","date":"2020-12-31","arxiv_id":"2012.15534","repositories_listed":0,"syntology":null},{"url":null,"slug":"seeing-is-knowing-fact-based-visual-question","title":"Seeing is Knowing! Fact-based Visual Question Answering using Knowledge Graph Embeddings","date":"2020-12-31","arxiv_id":"2012.15484","repositories_listed":0,"syntology":null},{"url":null,"slug":"studying-strategically-learning-to-mask-for","title":"Studying Strategically: Learning to Mask for Closed-book QA","date":"2020-12-31","arxiv_id":"2012.15856","repositories_listed":0,"syntology":null},{"url":"/paper/presenting-a-dataset-for-collaborator","slug":"presenting-a-dataset-for-collaborator","title":"Presenting a Dataset for Collaborator Recommending Systems in Academic Social Network: a Case Study on ReseachGate","date":"2020-12-29","arxiv_id":"2101.01141","repositories_listed":0,"syntology":null},{"url":null,"slug":"burt-bert-inspired-universal-representation-1","title":"BURT: BERT-inspired Universal Representation from Learning Meaningful Segment","date":"2020-12-28","arxiv_id":"2012.14320","repositories_listed":0,"syntology":null},{"url":null,"slug":"commonsense-visual-sensemaking-for-autonomous","title":"Commonsense Visual Sensemaking for Autonomous Driving: On Generalised Neurosymbolic Online Abduction Integrating Vision and Semantics","date":"2020-12-28","arxiv_id":"2012.14359","repositories_listed":0,"syntology":null}],"record_sha256":"a0bdc3d03a6daee08b767747f062b1d9e2b5dca2d54fd010ccda7b3bafce5739","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}