{"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/semantic-textual-similarity/papers/13","list_of":"/task/semantic-textual-similarity","task":"Semantic Textual Similarity","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":13,"pages_in_order":24,"rows_per_page":100,"rows":[1201,1300],"of":2381,"counts":{"archive_papers_tagged":2381,"with_a_code_link":693,"where_syntology_ran_a_sample":144,"not_listed_spam_title":0,"listed":2381,"listed_where_code_ran":144,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":118,"every_run_a_failure_of_syntologys_instrument":26,"listed_with_a_run_with_no_instrument_failure":118,"listed_every_run_a_failure_of_syntologys_instrument":26,"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/semantic-textual-similarity","prev":"/task/semantic-textual-similarity/papers/12","next":"/task/semantic-textual-similarity/papers/14","papers":[{"url":null,"slug":"semantic-similarity-computing-model-based-on","title":"Semantic Similarity Computing Model Based on Multi Model Fine-Grained Nonlinear Fusion","date":"2022-02-05","arxiv_id":"2202.02476","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":"language-models-explain-word-reading-times","title":"Language Models Explain Word Reading Times Better Than Empirical Predictability","date":"2022-02-02","arxiv_id":"2202.01128","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-based-approach-for-safety-signals","title":"AI-based Approach for Safety Signals Detection from Social Networks: Application to the Levothyrox Scandal in 2017 on Doctissimo Forum","date":"2022-02-01","arxiv_id":"2203.03538","repositories_listed":0,"syntology":null},{"url":null,"slug":"duplicate-detection-in-a-knowledge-base-with","title":"Duplicate Detection in a Knowledge Base with PIKA","date":"2022-02-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-embedding-of-semantic-similarity-in","title":"Efficient Embedding of Semantic Similarity in Control Policies via Entangled Bisimulation","date":"2022-01-28","arxiv_id":"2201.12300","repositories_listed":0,"syntology":null},{"url":null,"slug":"discriminative-supervised-subspace-learning","title":"Discriminative Supervised Subspace Learning for Cross-modal Retrieval","date":"2022-01-26","arxiv_id":"2201.11843","repositories_listed":0,"syntology":null},{"url":null,"slug":"ease-entity-aware-contrastive-learning-of","title":"EASE: Entity-Aware Contrastive Learning of Sentence Embedding","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-contextual-representation-with","title":"Improving Contextual Representation with Gloss Regularized Pre-training","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-monolingual-sentence-embeddings-with","title":"Learning Monolingual Sentence Embeddings with Large-scale Parallel Translation Datasets","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-with-large-action","title":"Reinforcement Learning with Large Action Spaces for Neural Machine Translation","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"task-formulation-matters-when-learning","title":"Task Formulation Matters When Learning Continuously: A Case Study in Visual Question Answering","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"structure-with-semantics-exploiting-document","title":"Structure and Semantics Preserving Document Representations","date":"2022-01-11","arxiv_id":"2201.03720","repositories_listed":0,"syntology":null},{"url":null,"slug":"resource-recommender-system-performance","title":"Graph-Based Recommendation System Enhanced with Community Detection","date":"2022-01-10","arxiv_id":"2201.03622","repositories_listed":0,"syntology":null},{"url":null,"slug":"traffic-event-description-based-on-twitter","title":"Traffic event description based on Twitter data using Unsupervised Learning Methods for Indian road conditions","date":"2021-12-23","arxiv_id":"2201.02738","repositories_listed":0,"syntology":null},{"url":null,"slug":"superpixel-based-building-damage-detection","title":"Superpixel-Based Building Damage Detection from Post-earthquake Imagery Using Deep Neural Networks","date":"2021-12-09","arxiv_id":"2112.04744","repositories_listed":0,"syntology":null},{"url":null,"slug":"classifying-verses-of-the-quran-using-doc2vec","title":"Classifying Verses of the Quran using Doc2vec","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-learning-of-sentence","title":"Contrastive Learning of Sentence Representations","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-domain-language-modeling-an-empirical","title":"Cross-Domain Language Modeling: An Empirical Investigation","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-lingual-alignment-of-knowledge-graph","title":"Cross-lingual Alignment of Knowledge Graph Triples with Sentences","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bilingual-topic-models-for-comparable-corpora","title":"Bilingual Topic Models for Comparable Corpora","date":"2021-11-30","arxiv_id":"2111.15278","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-compositional-zero-shot-learning-with","title":"3D Compositional Zero-shot Learning with DeCompositional Consensus","date":"2021-11-29","arxiv_id":"2111.14673","repositories_listed":0,"syntology":null},{"url":null,"slug":"code-clone-detection-based-on-event-embedding","title":"Code Clone Detection based on Event Embedding and Event Dependency","date":"2021-11-28","arxiv_id":"2111.14183","repositories_listed":0,"syntology":null},{"url":null,"slug":"simple-contrastive-representation-adversarial","title":"Simple Contrastive Representation Adversarial Learning for NLP Tasks","date":"2021-11-26","arxiv_id":"2111.13301","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-distributional-principles-for-the","title":"Using Distributional Principles for the Semantic Study of Contextual Language Models","date":"2021-11-23","arxiv_id":"2111.12174","repositories_listed":0,"syntology":null},{"url":null,"slug":"metamorphic-adversarial-detection-pipeline","title":"Metamorphic Adversarial Detection Pipeline for Face Recognition Systems","date":"2021-11-22","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-sentence-is-worth-128-pseudo-tokens-a","title":"A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"compressing-sentence-representation-via","title":"Compressing Sentence Representation via Homomorphic Projective Distillation","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"contfv-a-contrastive-learning-framework-for","title":"ConTFV: A Contrastive Learning Framework for Table-based Fact Verification","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"debiased-contrastive-learning-of-unsupervised","title":"Debiased Contrastive Learning of Unsupervised Sentence Representations","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"do-we-need-to-differentiate-negative","title":"Do We Need to Differentiate Negative Candidates Before Training a Neural Ranker?","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-named-entity-recognition-with-joint","title":"Few-shot Named Entity Recognition with Joint Token and Sentence Awareness","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"from-stance-to-concern-adaptation-of","title":"From Stance to Concern: Adaptation of Propositional Analysis to New Tasks and Domains","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-universal-sentence-embeddings-with","title":"Learning Universal Sentence Embeddings with Large-scale Parallel Translation Datasets","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-stage-distillation-framework-for-cross","title":"Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-keyphrase-generation-analysis-and","title":"Neural Keyphrase Generation: Analysis and Evaluation","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"newsfarm-the-largest-chinese-corpus-for-long","title":"NEWSFARM: the Largest Chinese Corpus for Long News Summarization","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"promptbert-improving-bert-sentence-embeddings","title":"PromptBERT: Improving BERT Sentence Embeddings with Prompts","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"qa4prf-a-question-answering-based-framework","title":"QA4PRF: A Question Answering based Framework for Pseudo Relevance Feedback","date":"2021-11-16","arxiv_id":"2111.08229","repositories_listed":0,"syntology":null},{"url":null,"slug":"relic-retrieving-evidence-for-literary-claims","title":"RELiC: Retrieving Evidence for Literary Claims","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rome-a-robust-metric-for-evaluating-natural","title":"RoMe: A Robust Metric for Evaluating Natural Language Generation","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-based-visual-question-answering","title":"Uncertainty-based Visual Question Answering: Estimating Semantic Inconsistency between Image and Knowledge Base","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/mnet-sim-a-multi-layered-semantic-similarity-1","slug":"mnet-sim-a-multi-layered-semantic-similarity-1","title":"MNet-Sim: A Multi-layered Semantic Similarity Network to Evaluate Sentence Similarity","date":"2021-11-09","arxiv_id":"2111.05412","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-text-autoencoder-from-transformer-for-fast","title":"A text autoencoder from transformer for fast encoding language representation","date":"2021-11-04","arxiv_id":"2111.02844","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-attention-networks-for-distilling","title":"Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation","date":"2021-11-03","arxiv_id":"2111.02100","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-effectiveness-of-using-internal","title":"Assessing Effectiveness of Using Internal Signals for Check-Worthy Claim Identification in Unlabeled Data for Automated Fact-Checking","date":"2021-11-02","arxiv_id":"2111.01706","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-decomposable-model-for-disentangling","title":"A Deep Decomposable Model for Disentangling Syntax and Semantics in Sentence Representation","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fine-grained-analysis-of-bertscore","title":"A Fine-Grained Analysis of BERTScore","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"anaphora-resolution-in-dialogue-description","title":"Anaphora Resolution in Dialogue: Description of the DFKI-TalkingRobots System for the CODI-CRAC 2021 Shared-Task","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-lingual-sentence-embedding-using-multi","title":"Cross-lingual Sentence Embedding using Multi-Task Learning","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-semantic-textual-similarity-and","title":"Incorporating Semantic Textual Similarity and Lexical Matching for Information Retrieval","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"locality-preserving-sentence-encoding","title":"Locality Preserving Sentence Encoding","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"searching-for-legal-documents-at-paragraph","title":"Searching for Legal Documents at Paragraph Level: Automating Label Generation and Use of an Extended Attention Mask for Boosting Neural Models of Semantic Similarity","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-alignment-with-calibrated-similarity","title":"Semantic Alignment with Calibrated Similarity for Multilingual Sentence Embedding","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uncle-explicitly-leveraging-semantic","title":"UnClE: Explicitly Leveraging Semantic Similarity to Reduce the Parameters of Word Embeddings","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"transaug-translate-as-augmentation-for","title":"TransAug: Translate as Augmentation for Sentence Embeddings","date":"2021-10-30","arxiv_id":"2111.00157","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-full-constituency-parsing-with","title":"Unsupervised Full Constituency Parsing with Neighboring Distribution Divergence","date":"2021-10-29","arxiv_id":"2110.15931","repositories_listed":0,"syntology":null},{"url":null,"slug":"facter-check-semi-automated-fact-checking","title":"FacTeR-Check: Semi-automated fact-checking through Semantic Similarity and Natural Language Inference","date":"2021-10-27","arxiv_id":"2110.14532","repositories_listed":0,"syntology":null},{"url":null,"slug":"mic-model-agnostic-integrated-cross-channel","title":"MIC: Model-agnostic Integrated Cross-channel Recommenders","date":"2021-10-22","arxiv_id":"2110.11570","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-stage-voice-application-recommender","title":"Two-stage Voice Application Recommender System for Unhandled Utterances in Intelligent Personal Assistant","date":"2021-10-19","arxiv_id":"2110.09877","repositories_listed":0,"syntology":null},{"url":null,"slug":"farfetched-an-entity-centric-approach-for","title":"FarFetched: An Entity-centric Approach for Reasoning on Textually Represented Environments","date":"2021-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-multi-modal-embeddings-from","title":"Efficient Multi-Modal Embeddings from Structured Data","date":"2021-10-06","arxiv_id":"2110.02577","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-single-trial-representational","title":"Using Single-Trial Representational Similarity Analysis with EEG to track semantic similarity in emotional word processing","date":"2021-10-04","arxiv_id":"2110.03529","repositories_listed":0,"syntology":null},{"url":null,"slug":"agnostic-personalized-federated-learning-with","title":"Agnostic Personalized Federated Learning with Kernel Factorization","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"what-makes-better-augmentation-strategies","title":"What Makes Better Augmentation Strategies? Augment Difficult but Not too Different","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cluster-analysis-with-deep-embeddings-and","title":"Cluster Analysis with Deep Embeddings and Contrastive Learning","date":"2021-09-26","arxiv_id":"2109.12714","repositories_listed":0,"syntology":null},{"url":null,"slug":"sorting-through-the-noise-testing-robustness","title":"Sorting through the noise: Testing robustness of information processing in pre-trained language models","date":"2021-09-25","arxiv_id":"2109.12393","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-crowd-sourcing-for-semantic","title":"Rethinking Crowd Sourcing for Semantic Similarity","date":"2021-09-24","arxiv_id":"2109.11969","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":"investigating-entropy-for-extractive-document","title":"Investigating Entropy for Extractive Document Summarization","date":"2021-09-22","arxiv_id":"2109.10886","repositories_listed":0,"syntology":null},{"url":"/paper/convfit-conversational-fine-tuning-of","slug":"convfit-conversational-fine-tuning-of","title":"ConvFiT: Conversational Fine-Tuning of Pretrained Language Models","date":"2021-09-21","arxiv_id":"2109.10126","repositories_listed":0,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/convfit-conversational-fine-tuning-of#ran","syntology_url":"https://syntology.ai/paper/2109.10126","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.10126"}},"official":null}},{"url":null,"slug":"adversarial-training-with-contrastive","title":"Adversarial Training with Contrastive Learning in NLP","date":"2021-09-19","arxiv_id":"2109.09075","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-word-embedding-learning-for","title":"Contrastive Word Embedding Learning for Neural Machine Translation","date":"2021-09-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"transformers-can-compose-skills-to-solve","title":"Transformers Can Compose Skills To Solve Novel Problems Without Finetuning","date":"2021-09-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-semantic-indexing-structure-for-image","title":"A Semantic Indexing Structure for Image Retrieval","date":"2021-09-14","arxiv_id":"2109.06583","repositories_listed":0,"syntology":null},{"url":"/paper/mural-multimodal-multitask-retrieval-across","slug":"mural-multimodal-multitask-retrieval-across","title":"MURAL: Multimodal, Multitask Retrieval Across Languages","date":"2021-09-10","arxiv_id":"2109.05125","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-content-creation-using","title":"Data Driven Content Creation using Statistical and Natural Language Processing Techniques for Financial Domain","date":"2021-09-07","arxiv_id":"2109.02935","repositories_listed":0,"syntology":null},{"url":null,"slug":"exposing-length-divergence-bias-of-textual","title":"On Length Divergence Bias in Textual Matching Models","date":"2021-09-06","arxiv_id":"2109.02431","repositories_listed":0,"syntology":null},{"url":"/paper/pr-net-preference-reasoning-for-personalized","slug":"pr-net-preference-reasoning-for-personalized","title":"PR-Net: Preference Reasoning for Personalized Video Highlight Detection","date":"2021-09-04","arxiv_id":"2109.01799","repositories_listed":0,"syntology":null},{"url":null,"slug":"aligning-cross-lingual-sentence","title":"Aligning Cross-lingual Sentence Representations with Dual Momentum Contrast","date":"2021-09-01","arxiv_id":"2109.00253","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-the-eligibility-of-backtranslated","title":"Assessing the Eligibility of Backtranslated Samples Based on Semantic Similarity for the Paraphrase Identification Task","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-datasets-for-cross-lingual","title":"Evaluation Datasets for Cross-lingual Semantic Textual Similarity","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"paragraph-similarity-matches-for-generating","title":"Paragraph Similarity Matches for Generating Multiple-choice Test Items","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sense-representations-for-portuguese","title":"Sense representations for Portuguese: experiments with sense embeddings and deep neural language models","date":"2021-08-31","arxiv_id":"2109.00025","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-aware-long-short-range-spatial","title":"Object-aware Long-short-range Spatial Alignment for Few-Shot Fine-Grained Image Classification","date":"2021-08-30","arxiv_id":"2108.13098","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiplex-graph-neural-network-for-extractive","title":"Multiplex Graph Neural Network for Extractive Text Summarization","date":"2021-08-29","arxiv_id":"2108.12870","repositories_listed":0,"syntology":null},{"url":null,"slug":"lingxi-a-diversity-aware-chinese-modern","title":"Lingxi: A Diversity-aware Chinese Modern Poetry Generation System","date":"2021-08-27","arxiv_id":"2108.12108","repositories_listed":0,"syntology":null},{"url":"/paper/multi-attributed-and-structured-text-to-face","slug":"multi-attributed-and-structured-text-to-face","title":"Multi-Attributed and Structured Text-to-Face Synthesis","date":"2021-08-25","arxiv_id":"2108.11100","repositories_listed":0,"syntology":null},{"url":null,"slug":"czech-news-dataset-for-semanic-textual","title":"Czech News Dataset for Semantic Textual Similarity","date":"2021-08-19","arxiv_id":"2108.08708","repositories_listed":0,"syntology":null},{"url":null,"slug":"tsi-an-ad-text-strength-indicator-using-text","title":"TSI: an Ad Text Strength Indicator using Text-to-CTR and Semantic-Ad-Similarity","date":"2021-08-18","arxiv_id":"2108.08226","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforce-attack-adversarial-attack-against","title":"Reinforce Attack: Adversarial Attack against BERT with Reinforcement Learning","date":"2021-08-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-answer-similarity-for-evaluating","title":"Semantic Answer Similarity for Evaluating Question Answering Models","date":"2021-08-13","arxiv_id":"2108.06130","repositories_listed":0,"syntology":null},{"url":"/paper/explanations-for-commonsenseqa-new-dataset","slug":"explanations-for-commonsenseqa-new-dataset","title":"Explanations for CommonsenseQA: New Dataset and Models","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"structure-aware-sentence-encoder-in-bert","title":"Structure-aware Sentence Encoder in Bert-Based Siamese Network","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"system-description-for-the-commongen-task","title":"System Description for the CommonGen task with the POINTER model","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"seed-words-based-data-selection-for-language","title":"Seed Words Based Data Selection for Language Model Adaptation","date":"2021-07-20","arxiv_id":"2107.09433","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-multilingual-models-the-best-choice-for","title":"Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan","date":"2021-07-16","arxiv_id":"2107.07903","repositories_listed":0,"syntology":null},{"url":"/paper/autobert-zero-evolving-bert-backbone-from","slug":"autobert-zero-evolving-bert-backbone-from","title":"AutoBERT-Zero: Evolving BERT Backbone from Scratch","date":"2021-07-15","arxiv_id":"2107.07445","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-level-graph-contrastive-learning","title":"Multi-Level Graph Contrastive Learning","date":"2021-07-06","arxiv_id":"2107.02639","repositories_listed":0,"syntology":null},{"url":null,"slug":"dtafa-decoupled-training-architecture-for","title":"DTAFA: Decoupled Training Architecture for Efficient FAQ Retrieval","date":"2021-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"02aa6a71106623ab5417c59ee3623d2de7228d3723d83e009cbe3cd2997e2085","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}