{"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/language-modeling/papers/112","list_of":"/task/language-modeling","task":"Language Modeling","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":112,"pages_in_order":142,"rows_per_page":100,"rows":[11101,11200],"of":14182,"counts":{"archive_papers_tagged":14182,"with_a_code_link":5620,"where_syntology_ran_a_sample":1894,"not_listed_spam_title":0,"listed":14182,"listed_where_code_ran":1894,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1580,"every_run_a_failure_of_syntologys_instrument":314,"listed_with_a_run_with_no_instrument_failure":1580,"listed_every_run_a_failure_of_syntologys_instrument":314,"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/language-modeling","prev":"/task/language-modeling/papers/111","next":"/task/language-modeling/papers/113","papers":[{"url":null,"slug":"learnable-dependency-based-double-graph","title":"Learnable Dependency-based Double Graph Structure for Aspect-based Sentiment Analysis","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"malm-mixing-augmented-language-modeling-for","title":"MALM: Mixing Augmented Language Modeling for Zero-Shot Machine Translation","date":"2022-10-01","arxiv_id":"2210.00320","repositories_listed":0,"syntology":null},{"url":null,"slug":"mattica-smm4h22-leveraging-sentiment-for","title":"mattica@SMM4H’22: Leveraging sentiment for stance & premise joint learning","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-guided-program-synthesis-of","title":"Neural-Guided Program Synthesis of Information Extraction Rules Using Self-Supervision","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pingantech-at-smm4h-task1-multiple-pre","title":"PingAnTech at SMM4H task1: Multiple pre-trained model approaches for Adverse Drug Reactions","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pln-cmm-at-socialdisner-improving-detection","title":"PLN CMM at SocialDisNER: Improving Detection of Disease Mentions in Tweets by Using Document-Level Features","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"speaker-clustering-in-textual-dialogue-with-1","title":"Speaker Clustering in Textual Dialogue with Pairwise Utterance Relation and Cross-corpus Dialogue Act Supervision","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"taking-actions-separately-a-bidirectionally","title":"Taking Actions Separately: A Bidirectionally-Adaptive Transfer Learning Method for Low-Resource Neural Machine Translation","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"team-ainlpml-mup-in-sdp-2021-scientific","title":"Team AINLPML @ MuP in SDP 2021: Scientific Document Summarization by End-to-End Extractive and Abstractive Approach","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-covid-that-wasnt-counterfactual","title":"The COVID That Wasn’t: Counterfactual Journalism Using GPT","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-only-chance-to-understand-machine","title":"The Only Chance to Understand: Machine Translation of the Severely Endangered Low-resource Languages of Eurasia","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-role-of-context-in-detecting-the-target","title":"The Role of Context in Detecting the Target of Hate Speech","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-making-the-most-of-pre-trained","title":"Towards Making the Most of Pre-trained Translation Model for Quality Estimation","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-learning-improves-french-cross","title":"Transfer Learning Improves French Cross-Domain Dialect Identification: NRC @ VarDial 2022","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"transferring-knowledge-from-structure-aware-1","title":"Transferring Knowledge from Structure-aware Self-attention Language Model to Sequence-to-Sequence Semantic Parsing","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-data-augmentation-for-aspect","title":"Unsupervised Data Augmentation for Aspect Based Sentiment Analysis","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-structured-content-plans-for-fine-1","title":"Using Structured Content Plans for Fine-grained Syntactic Control in Pretrained Language Model Generation","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-robustness-of-self-supervised-1","title":"Augmentation Invariant Discrete Representation for Generative Spoken Language Modeling","date":"2022-09-30","arxiv_id":"2209.15483","repositories_listed":0,"syntology":null},{"url":null,"slug":"bidirectional-language-models-are-also-few","title":"Bidirectional Language Models Are Also Few-shot Learners","date":"2022-09-29","arxiv_id":"2209.14500","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-text-classification-with-dual","title":"Few-shot Text Classification with Dual Contrastive Consistency","date":"2022-09-29","arxiv_id":"2209.15069","repositories_listed":0,"syntology":null},{"url":null,"slug":"i-speak-you-verify-toward-trustworthy-neural","title":"Toward Trustworthy Neural Program Synthesis","date":"2022-09-29","arxiv_id":"2210.00848","repositories_listed":0,"syntology":null},{"url":null,"slug":"repairing-bugs-in-python-assignments-using","title":"Repairing Bugs in Python Assignments Using Large Language Models","date":"2022-09-29","arxiv_id":"2209.14876","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-alignment-of-dialogue-agents-via","title":"Improving alignment of dialogue agents via targeted human judgements","date":"2022-09-28","arxiv_id":"2209.14375","repositories_listed":0,"syntology":null},{"url":null,"slug":"keyword-extraction-from-short-texts-with-a","title":"Keyword Extraction from Short Texts with a Text-To-Text Transfer Transformer","date":"2022-09-28","arxiv_id":"2209.14008","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-contrastive-learning-as-multi","title":"Supervised Contrastive Learning as Multi-Objective Optimization for Fine-Tuning Large Pre-trained Language Models","date":"2022-09-28","arxiv_id":"2209.14161","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-self-supervised-learning-for-1","title":"End-to-End Lyrics Recognition with Self-supervised Learning","date":"2022-09-26","arxiv_id":"2209.12702","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-chess-with-language-models-and","title":"Learning Chess With Language Models and Transformers","date":"2022-09-24","arxiv_id":"2209.11902","repositories_listed":0,"syntology":null},{"url":null,"slug":"lgdn-language-guided-denoising-network-for","title":"LGDN: Language-Guided Denoising Network for Video-Language Modeling","date":"2022-09-23","arxiv_id":"2209.11388","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-page-creation-for","title":"Deep Learning Based Page Creation for Improving E-Commerce Organic Search Traffic","date":"2022-09-22","arxiv_id":"2209.10792","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompting-for-a-conversation-how-to-control-a","title":"Prompting for a conversation: How to control a dialog model?","date":"2022-09-22","arxiv_id":"2209.11068","repositories_listed":0,"syntology":null},{"url":null,"slug":"welm-a-well-read-pre-trained-language-model","title":"WeLM: A Well-Read Pre-trained Language Model for Chinese","date":"2022-09-21","arxiv_id":"2209.10372","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalizing-through-forgetting-domain","title":"Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes","date":"2022-09-20","arxiv_id":"2209.09485","repositories_listed":0,"syntology":null},{"url":null,"slug":"linguist-language-model-instruction-tuning-to","title":"LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging","date":"2022-09-20","arxiv_id":"2209.09900","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-generation-of-interpretable-inference","title":"NELLIE: A Neuro-Symbolic Inference Engine for Grounded, Compositional, and Explainable Reasoning","date":"2022-09-16","arxiv_id":"2209.07662","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-offline-reinforcement-learning-help","title":"Can Offline Reinforcement Learning Help Natural Language Understanding?","date":"2022-09-15","arxiv_id":"2212.03864","repositories_listed":0,"syntology":null},{"url":"/paper/omnivl-one-foundation-model-for-image","slug":"omnivl-one-foundation-model-for-image","title":"OmniVL:One Foundation Model for Image-Language and Video-Language Tasks","date":"2022-09-15","arxiv_id":"2209.07526","repositories_listed":0,"syntology":null},{"url":null,"slug":"ptab-using-the-pre-trained-language-model-for","title":"PTab: Using the Pre-trained Language Model for Modeling Tabular Data","date":"2022-09-15","arxiv_id":"2209.08060","repositories_listed":0,"syntology":null},{"url":null,"slug":"uchecker-masked-pretrained-language-models-as","title":"uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers","date":"2022-09-15","arxiv_id":"2209.07068","repositories_listed":0,"syntology":null},{"url":null,"slug":"bangla-wave-improving-bangla-automatic-speech","title":"Bangla-Wave: Improving Bangla Automatic Speech Recognition Utilizing N-gram Language Models","date":"2022-09-13","arxiv_id":"2209.12650","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-code-style-transfer-with-neural","title":"Exploring Code Style Transfer with Neural Networks","date":"2022-09-13","arxiv_id":"2209.06273","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-language-model-prompting-in-support","title":"Improving Language Model Prompting in Support of Semi-autonomous Task Learning","date":"2022-09-13","arxiv_id":"2209.07636","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-complex-network-based-graph-embedding","title":"A Complex Network based Graph Embedding Method for Link Prediction","date":"2022-09-11","arxiv_id":"2209.04884","repositories_listed":0,"syntology":null},{"url":null,"slug":"applying-wav2vec2-for-speech-recognition-on","title":"Applying wav2vec2 for Speech Recognition on Bengali Common Voices Dataset","date":"2022-09-11","arxiv_id":"2209.06581","repositories_listed":0,"syntology":null},{"url":null,"slug":"opal-ontology-aware-pretrained-language-model","title":"OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue","date":"2022-09-10","arxiv_id":"2209.04595","repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-transformer-language-model-for","title":"Multilingual Transformer Language Model for Speech Recognition in Low-resource Languages","date":"2022-09-08","arxiv_id":"2209.04041","repositories_listed":0,"syntology":null},{"url":null,"slug":"blessing-of-class-diversity-in-pre-training-1","title":"Blessing of Class Diversity in Pre-training","date":"2022-09-07","arxiv_id":"2209.03447","repositories_listed":0,"syntology":null},{"url":null,"slug":"distilling-the-knowledge-of-bert-for-ctc","title":"Distilling the Knowledge of BERT for CTC-based ASR","date":"2022-09-05","arxiv_id":"2209.02030","repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-language-adaptive-mutual-decoder-for","title":"Vision-Language Adaptive Mutual Decoder for OOV-STR","date":"2022-09-02","arxiv_id":"2209.00859","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-semantic-understanding-with-self","title":"Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization","date":"2022-09-01","arxiv_id":"2209.00278","repositories_listed":0,"syntology":null},{"url":null,"slug":"prefix-embeddings-for-in-context-machine","title":"Prefix Embeddings for In-context Machine Translation","date":"2022-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"udapter-typology-based-language-adapters-for","title":"UDapter: Typology-based Language Adapters for Multilingual Dependency Parsing and Sequence Labeling","date":"2022-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-sparsely-activated-transformers","title":"Efficient Sparsely Activated Transformers","date":"2022-08-31","arxiv_id":"2208.14580","repositories_listed":0,"syntology":null},{"url":null,"slug":"lifelong-learning-for-question-answering-with","title":"Continuous QA Learning with Structured Prompts","date":"2022-08-31","arxiv_id":"2208.14602","repositories_listed":0,"syntology":null},{"url":"/paper/efficient-and-interpretable-neural-models-for","slug":"efficient-and-interpretable-neural-models-for","title":"Efficient and Interpretable Neural Models for Entity Tracking","date":"2022-08-30","arxiv_id":"2208.14252","repositories_listed":0,"syntology":null},{"url":null,"slug":"logicrank-logic-induced-reranking-for","title":"LogicRank: Logic Induced Reranking for Generative Text-to-Image Systems","date":"2022-08-29","arxiv_id":"2208.13518","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-unsupervised-training-of-link-grammar","title":"On Unsupervised Training of Link Grammar Based Language Models","date":"2022-08-27","arxiv_id":"2208.13021","repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-biomedical-factual-knowledge-using","title":"Extracting Biomedical Factual Knowledge Using Pretrained Language Model and Electronic Health Record Context","date":"2022-08-26","arxiv_id":"2209.07859","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-a-t5-using-lab-sized-resources","title":"Training a T5 Using Lab-sized Resources","date":"2022-08-25","arxiv_id":"2208.12097","repositories_listed":0,"syntology":null},{"url":null,"slug":"peer-a-collaborative-language-model","title":"PEER: A Collaborative Language Model","date":"2022-08-24","arxiv_id":"2208.11663","repositories_listed":0,"syntology":null},{"url":null,"slug":"clower-a-pre-trained-language-model-with","title":"CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations","date":"2022-08-23","arxiv_id":"2208.10844","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluate-confidence-instead-of-perplexity-for","title":"Evaluate Confidence Instead of Perplexity for Zero-shot Commonsense Reasoning","date":"2022-08-23","arxiv_id":"2208.11007","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-syntax-aware-bert-for-identifying-well","title":"A Syntax Aware BERT for Identifying Well-Formed Queries in a Curriculum Framework","date":"2022-08-21","arxiv_id":"2208.09912","repositories_listed":0,"syntology":null},{"url":"/paper/gretel-graph-contrastive-topic-enhanced","slug":"gretel-graph-contrastive-topic-enhanced","title":"GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization","date":"2022-08-21","arxiv_id":"2208.09982","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-diverse-knowledge-sources-for","title":"Integrating Diverse Knowledge Sources for Online One-shot Learning of Novel Tasks","date":"2022-08-19","arxiv_id":"2208.09554","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-augmented-cyclic-learning-framework-for","title":"Graph-Augmented Cyclic Learning Framework for Similarity Estimation of Medical Clinical Notes","date":"2022-08-19","arxiv_id":"2208.09437","repositories_listed":0,"syntology":null},{"url":null,"slug":"vlmae-vision-language-masked-autoencoder","title":"VLMAE: Vision-Language Masked Autoencoder","date":"2022-08-19","arxiv_id":"2208.09374","repositories_listed":0,"syntology":null},{"url":null,"slug":"open-information-extraction-from-2007-to-2022","title":"A Survey on Open Information Extraction from Rule-based Model to Large Language Model","date":"2022-08-18","arxiv_id":"2208.08690","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-comparison-of-language-model","title":"Visual Comparison of Language Model Adaptation","date":"2022-08-17","arxiv_id":"2208.08176","repositories_listed":0,"syntology":null},{"url":null,"slug":"cloud-based-real-time-molecular-screening","title":"Cloud-Based Real-Time Molecular Screening Platform with MolFormer","date":"2022-08-13","arxiv_id":"2208.06665","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-retraining-by-recycling-parameter","title":"Reducing Retraining by Recycling Parameter-Efficient Prompts","date":"2022-08-10","arxiv_id":"2208.05577","repositories_listed":0,"syntology":null},{"url":null,"slug":"introducing-berel-bert-embeddings-for","title":"Introducing BEREL: BERT Embeddings for Rabbinic-Encoded Language","date":"2022-08-03","arxiv_id":"2208.01875","repositories_listed":0,"syntology":null},{"url":null,"slug":"masked-vision-and-language-modeling-for-multi","title":"Masked Vision and Language Modeling for Multi-modal Representation Learning","date":"2022-08-03","arxiv_id":"2208.02131","repositories_listed":0,"syntology":null},{"url":null,"slug":"vq-t-rnn-transducers-using-vector-quantized","title":"VQ-T: RNN Transducers using Vector-Quantized Prediction Network States","date":"2022-08-03","arxiv_id":"2208.01818","repositories_listed":0,"syntology":null},{"url":null,"slug":"dictbert-dictionary-description-knowledge","title":"DictBERT: Dictionary Description Knowledge Enhanced Language Model Pre-training via Contrastive Learning","date":"2022-08-01","arxiv_id":"2208.00635","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-flowsheets-a-generative","title":"Learning from flowsheets: A generative transformer model for autocompletion of flowsheets","date":"2022-08-01","arxiv_id":"2208.00859","repositories_listed":0,"syntology":null},{"url":null,"slug":"augmenting-vision-language-pretraining-by","title":"Augmenting Vision Language Pretraining by Learning Codebook with Visual Semantics","date":"2022-07-31","arxiv_id":"2208.00475","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-knowledge-bank-for-pretrained","title":"Neural Knowledge Bank for Pretrained Transformers","date":"2022-07-31","arxiv_id":"2208.00399","repositories_listed":0,"syntology":null},{"url":null,"slug":"entity-type-prediction-leveraging-graph-walks","title":"Entity Type Prediction Leveraging Graph Walks and Entity Descriptions","date":"2022-07-28","arxiv_id":"2207.14094","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hazard-analysis-framework-for-code","title":"A Hazard Analysis Framework for Code Synthesis Large Language Models","date":"2022-07-25","arxiv_id":"2207.14157","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-transformer-based-neural-language-model","title":"A Transformer-based Neural Language Model that Synthesizes Brain Activation Maps from Free-Form Text Queries","date":"2022-07-24","arxiv_id":"2208.00840","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-models-of-protein-sequences-at-the","title":"Language models of protein sequences at the scale of evolution enable accurate structure prediction","date":"2022-07-21","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"stt-soft-template-tuning-for-few-shot-1","title":"STT: Soft Template Tuning for Few-Shot Adaptation","date":"2022-07-18","arxiv_id":"2207.08408","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-the-human-global-context-does-the","title":"Towards the Human Global Context: Does the Vision-Language Model Really Judge Like a Human Being?","date":"2022-07-18","arxiv_id":"2207.08333","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-large-vocabulary-neural-language","title":"Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices","date":"2022-07-18","arxiv_id":"2207.08988","repositories_listed":0,"syntology":null},{"url":null,"slug":"word-play-for-playing-othello-reverses","title":"Word Play for Playing Othello (Reverses)","date":"2022-07-18","arxiv_id":"2207.08766","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-overview-of-distant-supervision-for","title":"An Overview of Distant Supervision for Relation Extraction with a Focus on Denoising and Pre-training Methods","date":"2022-07-17","arxiv_id":"2207.08286","repositories_listed":0,"syntology":null},{"url":null,"slug":"natural-language-processing-for","title":"Natural language processing for clusterization of genes according to their functions","date":"2022-07-17","arxiv_id":"2207.08162","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-dialog-systems-with-dual-knowledge","title":"Multimodal Dialog Systems with Dual Knowledge-enhanced Generative Pretrained Language Model","date":"2022-07-16","arxiv_id":"2207.07934","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-no-code-low-code-paradigm-for-authoring","title":"A No-Code Low-Code Paradigm for Authoring Business Automations Using Natural Language","date":"2022-07-15","arxiv_id":"2207.10648","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-flexible-translation-between-robot","title":"Learning Flexible Translation between Robot Actions and Language Descriptions","date":"2022-07-15","arxiv_id":"2207.07437","repositories_listed":0,"syntology":null},{"url":null,"slug":"bertin-efficient-pre-training-of-a-spanish","title":"BERTIN: Efficient Pre-Training of a Spanish Language Model using Perplexity Sampling","date":"2022-07-14","arxiv_id":"2207.06814","repositories_listed":0,"syntology":null},{"url":null,"slug":"beware-the-rationalization-trap-when-language","title":"Beware the Rationalization Trap! When Language Model Explainability Diverges from our Mental Models of Language","date":"2022-07-14","arxiv_id":"2207.06897","repositories_listed":0,"syntology":null},{"url":null,"slug":"confident-adaptive-language-modeling","title":"Confident Adaptive Language Modeling","date":"2022-07-14","arxiv_id":"2207.07061","repositories_listed":0,"syntology":null},{"url":null,"slug":"layout-aware-information-extraction-for","title":"Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration","date":"2022-07-14","arxiv_id":"2207.06717","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-data-to-text-generation-based-on-small","title":"Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model","date":"2022-07-14","arxiv_id":"2207.06839","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-transfer-learning-based-model-for-text","title":"A Transfer Learning Based Model for Text Readability Assessment in German","date":"2022-07-13","arxiv_id":"2207.06265","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-driven-emotional-style-control-and-cross","title":"Text-driven Emotional Style Control and Cross-speaker Style Transfer in Neural TTS","date":"2022-07-13","arxiv_id":"2207.06000","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-deberta-based-model-for-financial","title":"A Novel DeBERTa-based Model for Financial Question Answering Task","date":"2022-07-12","arxiv_id":"2207.05875","repositories_listed":0,"syntology":null},{"url":null,"slug":"internal-language-model-estimation-based","title":"Internal Language Model Estimation based Language Model Fusion for Cross-Domain Code-Switching Speech Recognition","date":"2022-07-09","arxiv_id":"2207.04176","repositories_listed":0,"syntology":null},{"url":null,"slug":"asr-generated-text-for-language-model-pre","title":"ASR-Generated Text for Language Model Pre-training Applied to Speech Tasks","date":"2022-07-05","arxiv_id":"2207.01893","repositories_listed":0,"syntology":null}],"record_sha256":"9619aeb1414564a43577050b1522d4df07adf0bda5be10e294a52738da5f66f9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}