{"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-modelling/papers/149","list_of":"/task/language-modelling","task":"Language Modelling","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":149,"pages_in_order":177,"rows_per_page":100,"rows":[14801,14900],"of":17610,"counts":{"archive_papers_tagged":17610,"with_a_code_link":7012,"where_syntology_ran_a_sample":2428,"not_listed_spam_title":0,"listed":17610,"listed_where_code_ran":2428,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2027,"every_run_a_failure_of_syntologys_instrument":401,"listed_with_a_run_with_no_instrument_failure":2027,"listed_every_run_a_failure_of_syntologys_instrument":401,"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-modelling","prev":"/task/language-modelling/papers/148","next":"/task/language-modelling/papers/150","papers":[{"url":null,"slug":"investigating-methods-to-improve-language","title":"Investigating Methods to Improve Language Model Integration for Attention-based Encoder-Decoder ASR Models","date":"2021-04-12","arxiv_id":"2104.05544","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-autoregressive-transformer-based-end-to","title":"Non-autoregressive Transformer-based End-to-end ASR using BERT","date":"2021-04-10","arxiv_id":"2104.04805","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-model-fusion-for-streaming-end-to","title":"Language model fusion for streaming end to end speech recognition","date":"2021-04-09","arxiv_id":"2104.04487","repositories_listed":0,"syntology":null},{"url":null,"slug":"lookup-table-recurrent-language-models-for","title":"Lookup-Table Recurrent Language Models for Long Tail Speech Recognition","date":"2021-04-09","arxiv_id":"2104.04552","repositories_listed":0,"syntology":null},{"url":null,"slug":"extended-parallel-corpus-for-amharic-english","title":"Extended Parallel Corpus for Amharic-English Machine Translation","date":"2021-04-08","arxiv_id":"2104.03543","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpreting-a-pre-trained-model-is-a-key-for","title":"Interpreting A Pre-trained Model Is A Key For Model Architecture Optimization: A Case Study On Wav2Vec 2.0","date":"2021-04-07","arxiv_id":"2104.02851","repositories_listed":0,"syntology":null},{"url":null,"slug":"pushing-the-limits-of-non-autoregressive","title":"Pushing the Limits of Non-Autoregressive Speech Recognition","date":"2021-04-07","arxiv_id":"2104.03416","repositories_listed":0,"syntology":null},{"url":null,"slug":"communication-efficient-agnostic-federated","title":"Communication-Efficient Agnostic Federated Averaging","date":"2021-04-06","arxiv_id":"2104.02748","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextualized-streaming-end-to-end-speech","title":"Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion","date":"2021-04-05","arxiv_id":"2104.02194","repositories_listed":0,"syntology":null},{"url":null,"slug":"covid-19-sentiment-analysis-via-deep-learning","title":"COVID-19 sentiment analysis via deep learning during the rise of novel cases","date":"2021-04-05","arxiv_id":"2104.10662","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-distance-a-new-metric-for-asr","title":"Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding","date":"2021-04-05","arxiv_id":"2104.02138","repositories_listed":0,"syntology":null},{"url":"/paper/speechstew-simply-mix-all-available-speech","slug":"speechstew-simply-mix-all-available-speech","title":"SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network","date":"2021-04-05","arxiv_id":"2104.02133","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-automated-psychotherapy-via-language","title":"Towards Automated Psychotherapy via Language Modeling","date":"2021-04-05","arxiv_id":"2104.10661","repositories_listed":0,"syntology":null},{"url":null,"slug":"indt5-a-text-to-text-transformer-for-10","title":"IndT5: A Text-to-Text Transformer for 10 Indigenous Languages","date":"2021-04-04","arxiv_id":"2104.07483","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfornn-capturing-the-sequential","title":"TransfoRNN: Capturing the Sequential Information in Self-Attention Representations for Language Modeling","date":"2021-04-04","arxiv_id":"2104.01572","repositories_listed":0,"syntology":null},{"url":null,"slug":"arabic-compact-language-modelling-for","title":"Arabic Compact Language Modelling for Resource Limited Devices","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bertic-the-transformer-language-model-for-1","title":"BERTić - The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-over-under-translation-errors-for","title":"Detecting over/under-translation errors for determining adequacy in human translations","date":"2021-04-01","arxiv_id":"2104.00267","repositories_listed":0,"syntology":null},{"url":null,"slug":"dlrg-dravidianlangtech-eacl2021-transformer","title":"DLRG@DravidianLangTech-EACL2021: Transformer based approachfor Offensive Language Identification on Code-Mixed Tamil","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"does-she-wink-or-does-she-nod-a-challenging","title":"Does She Wink or Does She Nod? A Challenging Benchmark for Evaluating Word Understanding of Language Models","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-unsupervised-nmt-for-related","title":"Efficient Unsupervised NMT for Related Languages with Cross-Lingual Language Models and Fidelity Objectives","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"emotional-robbert-and-insensitive-bertje","title":"Emotional RobBERT and Insensitive BERTje: Combining Transformers and Affect Lexica for Dutch Emotion Detection","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"expressive-text-to-speech-using-style-tag","title":"Expressive Text-to-Speech using Style Tag","date":"2021-04-01","arxiv_id":"2104.00436","repositories_listed":0,"syntology":null},{"url":null,"slug":"globalizing-bert-based-transformer","title":"Globalizing BERT-based Transformer Architectures for Long Document Summarization","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"introducing-a-large-tunisian-arabizi","title":"Introducing A large Tunisian Arabizi Dialectal Dataset for Sentiment Analysis","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"is-supervised-syntactic-parsing-beneficial-1","title":"Is Supervised Syntactic Parsing Beneficial for Language Understanding Tasks? An Empirical Investigation","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/keep-learning-self-supervised-meta-learning","slug":"keep-learning-self-supervised-meta-learning","title":"Keep Learning: Self-supervised Meta-learning for Learning from Inference","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"maoqin-dravidianlangtech-eacl2021-the","title":"Maoqin @ DravidianLangTech-EACL2021: The Application of Transformer-Based Model","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"maximal-multiverse-learning-for-promoting","title":"Maximal Multiverse Learning for Promoting Cross-Task Generalization of Fine-Tuned Language Models","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mucs-lt-edi-eacl2021-cohope-hope-speech","title":"MUCS@LT-EDI-EACL2021:CoHope-Hope Speech Detection for Equality, Diversity, and Inclusion in Code-Mixed Texts","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-slavic-named-entity-recognition","title":"Multilingual Slavic Named Entity Recognition","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-computational-modelling-of-michif","title":"On the Computational Modelling of Michif Verbal Morphology","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"one-toward-one-model-one-algorithm-one-corpus","title":"ONE: Toward ONE model, ONE algorithm, ONE corpus dedicated to sentiment analysis of Arabic/Arabizi and its dialects","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pseudo-label-guided-unsupervised-domain","title":"Pseudo-Label Guided Unsupervised Domain Adaptation of Contextual Embeddings","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"quranic-verses-semantic-relatedness-using","title":"Quranic Verses Semantic Relatedness Using AraBERT","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"story-centaur-large-language-model-few-shot","title":"Story Centaur: Large Language Model Few Shot Learning as a Creative Writing Tool","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"structural-encoding-and-pre-training-matter","title":"Structural Encoding and Pre-training Matter: Adapting BERT for Table-Based Fact Verification","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"team-hub-lt-edi-eacl2021-hope-speech","title":"TEAM HUB@LT-EDI-EACL2021: Hope Speech Detection Based On Pre-trained Language Model","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uc2-universal-cross-lingual-cross-modal","title":"UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training","date":"2021-04-01","arxiv_id":"2104.00332","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-does-bert-learn-from-arabic-machine","title":"What does BERT Learn from Arabic Machine Reading Comprehension Datasets?","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-encoder-learning-and-stream-fusion-for","title":"Multi-Encoder Learning and Stream Fusion for Transformer-Based End-to-End Automatic Speech Recognition","date":"2021-03-31","arxiv_id":"2104.00120","repositories_listed":0,"syntology":null},{"url":null,"slug":"afriki-machine-in-the-loop-afrikaans-poetry","title":"AfriKI: Machine-in-the-Loop Afrikaans Poetry Generation","date":"2021-03-30","arxiv_id":"2103.16190","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-image-text-pre-training-with","title":"Self-supervised Image-text Pre-training With Mixed Data In Chest X-rays","date":"2021-03-30","arxiv_id":"2103.16022","repositories_listed":0,"syntology":null},{"url":null,"slug":"entity-context-graph-learning-entity","title":"Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web","date":"2021-03-29","arxiv_id":"2103.15950","repositories_listed":0,"syntology":null},{"url":null,"slug":"retraining-distilbert-for-a-voice-shopping","title":"Retraining DistilBERT for a Voice Shopping Assistant by Using Universal Dependencies","date":"2021-03-29","arxiv_id":"2103.15737","repositories_listed":0,"syntology":null},{"url":null,"slug":"bart-based-semantic-correction-for-mandarin","title":"BART based semantic correction for Mandarin automatic speech recognition system","date":"2021-03-26","arxiv_id":"2104.05507","repositories_listed":0,"syntology":null},{"url":null,"slug":"correcting-automated-and-manual-speech","title":"Correcting Automated and Manual Speech Transcription Errors using Warped Language Models","date":"2021-03-26","arxiv_id":"2103.14580","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-approach-to-improve-robustness-of-nlp","title":"An Approach to Improve Robustness of NLP Systems against ASR Errors","date":"2021-03-25","arxiv_id":"2103.13610","repositories_listed":0,"syntology":null},{"url":null,"slug":"k-xlnet-a-general-method-for-combining","title":"K-XLNet: A General Method for Combining Explicit Knowledge with Language Model Pretraining","date":"2021-03-25","arxiv_id":"2104.10649","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-grounding-strategies-for-text-only","title":"Visual Grounding Strategies for Text-Only Natural Language Processing","date":"2021-03-25","arxiv_id":"2103.13942","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-resource-machine-translation-for-low","title":"Low-Resource Machine Translation Training Curriculum Fit for Low-Resource Languages","date":"2021-03-24","arxiv_id":"2103.13272","repositories_listed":0,"syntology":null},{"url":null,"slug":"thinking-aloud-dynamic-context-generation","title":"Thinking Aloud: Dynamic Context Generation Improves Zero-Shot Reasoning Performance of GPT-2","date":"2021-03-24","arxiv_id":"2103.13033","repositories_listed":0,"syntology":null},{"url":null,"slug":"hallucination-of-speech-recognition-errors","title":"Hallucination of speech recognition errors with sequence to sequence learning","date":"2021-03-23","arxiv_id":"2103.12258","repositories_listed":0,"syntology":null},{"url":null,"slug":"variable-name-recovery-in-decompiled-binary","title":"Variable Name Recovery in Decompiled Binary Code using Constrained Masked Language Modeling","date":"2021-03-23","arxiv_id":"2103.12801","repositories_listed":0,"syntology":null},{"url":null,"slug":"play-the-shannon-game-with-language-models-a","title":"Play the Shannon Game With Language Models: A Human-Free Approach to Summary Evaluation","date":"2021-03-19","arxiv_id":"2103.10918","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-rnn-transducer-technology-for","title":"Advancing RNN Transducer Technology for Speech Recognition","date":"2021-03-17","arxiv_id":"2103.09935","repositories_listed":0,"syntology":null},{"url":null,"slug":"set-to-sequence-methods-in-machine-learning-a","title":"Set-to-Sequence Methods in Machine Learning: a Review","date":"2021-03-17","arxiv_id":"2103.09656","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-few-shot-fact-checking-via-perplexity","title":"Towards Few-Shot Fact-Checking via Perplexity","date":"2021-03-17","arxiv_id":"2103.09535","repositories_listed":0,"syntology":null},{"url":"/paper/transformer-based-asr-incorporating-time","slug":"transformer-based-asr-incorporating-time","title":"Transformer-based ASR Incorporating Time-reduction Layer and Fine-tuning with Self-Knowledge Distillation","date":"2021-03-17","arxiv_id":"2103.09903","repositories_listed":0,"syntology":null},{"url":null,"slug":"claim-verification-using-a-multi-gan-based","title":"Claim Verification using a Multi-GAN based Model","date":"2021-03-14","arxiv_id":"2103.08001","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-a-word-level-language-model-with","title":"Learning a Word-Level Language Model with Sentence-Level Noise Contrastive Estimation for Contextual Sentence Probability Estimation","date":"2021-03-14","arxiv_id":"2103.07875","repositories_listed":0,"syntology":null},{"url":null,"slug":"embedding-calibration-for-music-semantic","title":"Optimal Embedding Calibration for Symbolic Music Similarity","date":"2021-03-13","arxiv_id":"2103.07656","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-diversity-of-neural-text-generation","title":"Improving Diversity of Neural Text Generation via Inverse Probability Weighting","date":"2021-03-13","arxiv_id":"2103.07649","repositories_listed":0,"syntology":null},{"url":null,"slug":"bilingual-dictionary-based-language-model","title":"Bilingual Dictionary-based Language Model Pretraining for Neural Machine Translation","date":"2021-03-12","arxiv_id":"2103.07040","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-morphological-embeddings-for-2","title":"Evaluation of Morphological Embeddings for English and Russian Languages","date":"2021-03-11","arxiv_id":"2103.06884","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-feature-weights-using-reward","title":"Learning Feature Weights using Reward Modeling for Denoising Parallel Corpora","date":"2021-03-11","arxiv_id":"2103.06968","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-improving-deep-learning-trace-analysis","title":"On Improving Deep Learning Trace Analysis with System Call Arguments","date":"2021-03-11","arxiv_id":"2103.06915","repositories_listed":0,"syntology":null},{"url":null,"slug":"relational-weight-priors-in-neural-networks","title":"Relational Weight Priors in Neural Networks for Abstract Pattern Learning and Language Modelling","date":"2021-03-10","arxiv_id":"2103.06198","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-context-free-and-contextualized","title":"Combining Context-Free and Contextualized Representations for Arabic Sarcasm Detection and Sentiment Identification","date":"2021-03-09","arxiv_id":"2103.05683","repositories_listed":0,"syntology":null},{"url":null,"slug":"mtlhealth-a-deep-learning-system-for","title":"MTLHealth: A Deep Learning System for Detecting Disturbing Content in Student Essays","date":"2021-03-07","arxiv_id":"2103.04290","repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-semantic-process-information-from","title":"Extracting Semantic Process Information from the Natural Language in Event Logs","date":"2021-03-06","arxiv_id":"2103.11761","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":"/paper/random-feature-attention-1","slug":"random-feature-attention-1","title":"Random Feature Attention","date":"2021-03-03","arxiv_id":"2103.02143","repositories_listed":0,"syntology":null},{"url":null,"slug":"university-of-copenhagen-participation-in","title":"University of Copenhagen Participation in TREC Health Misinformation Track 2020","date":"2021-03-03","arxiv_id":"2103.02462","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-rediscovery-hypothesis-language-models","title":"The Rediscovery Hypothesis: Language Models Need to Meet Linguistics","date":"2021-03-02","arxiv_id":"2103.01819","repositories_listed":0,"syntology":null},{"url":null,"slug":"long-document-summarization-in-a-low-resource","title":"Long Document Summarization in a Low Resource Setting using Pretrained Language Models","date":"2021-03-01","arxiv_id":"2103.00751","repositories_listed":0,"syntology":null},{"url":null,"slug":"unbiased-sentence-encoder-for-large-scale","title":"Unbiased Sentence Encoder For Large-Scale Multi-lingual Search Engines","date":"2021-03-01","arxiv_id":"2106.07719","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simple-but-effective-approach-to-n-shot","title":"N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking","date":"2021-02-27","arxiv_id":"2103.00293","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-primer-on-contrastive-pretraining-in","title":"A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives","date":"2021-02-25","arxiv_id":"2102.12982","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-universal-language-model-to-downstream","title":"From Universal Language Model to Downstream Task: Improving RoBERTa-Based Vietnamese Hate Speech Detection","date":"2021-02-24","arxiv_id":"2102.12162","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-modality-transition-for-image","title":"Enhanced Modality Transition for Image Captioning","date":"2021-02-23","arxiv_id":"2102.11526","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolutionary-optimization-of-contexts-for","title":"Evolutionary optimization of contexts for phonetic correction in speech recognition systems","date":"2021-02-23","arxiv_id":"2102.11480","repositories_listed":0,"syntology":null},{"url":null,"slug":"bilingual-language-modeling-a-transfer","title":"Bilingual Language Modeling, A transfer learning technique for Roman Urdu","date":"2021-02-22","arxiv_id":"2102.10958","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-human-readable-transcript-for","title":"Generating Human Readable Transcript for Automatic Speech Recognition with Pre-trained Language Model","date":"2021-02-22","arxiv_id":"2102.11114","repositories_listed":0,"syntology":null},{"url":null,"slug":"web-based-application-for-detecting","title":"Web-based Application for Detecting Indonesian Clickbait Headlines using IndoBERT","date":"2021-02-21","arxiv_id":"2102.10601","repositories_listed":0,"syntology":null},{"url":null,"slug":"alternate-endings-improving-prosody-for","title":"Alternate Endings: Improving Prosody for Incremental Neural TTS with Predicted Future Text Input","date":"2021-02-19","arxiv_id":"2102.09914","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-study-on-measuring-the","title":"An Empirical Study on Measuring the Similarity of Sentential Arguments with Language Model Domain Adaptation","date":"2021-02-19","arxiv_id":"2102.09786","repositories_listed":0,"syntology":null},{"url":null,"slug":"fixing-errors-of-the-google-voice-recognizer","title":"Fixing Errors of the Google Voice Recognizer through Phonetic Distance Metrics","date":"2021-02-18","arxiv_id":"2102.09680","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-end-to-end-speech-recognition-models-care","title":"Do End-to-End Speech Recognition Models Care About Context?","date":"2021-02-17","arxiv_id":"2102.09928","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-transformer-based-large-context","title":"Hierarchical Transformer-based Large-Context End-to-end ASR with Large-Context Knowledge Distillation","date":"2021-02-16","arxiv_id":"2102.07935","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-context-conversational-representation","title":"Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documents","date":"2021-02-16","arxiv_id":"2102.08147","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-end-to-end-speech-recognition-via-non","title":"Fast End-to-End Speech Recognition via Non-Autoregressive Models and Cross-Modal Knowledge Transferring from BERT","date":"2021-02-15","arxiv_id":"2102.07594","repositories_listed":0,"syntology":null},{"url":null,"slug":"jira-a-kurdish-speech-recognition-system","title":"Jira: a Kurdish Speech Recognition System Designing and Building Speech Corpus and Pronunciation Lexicon","date":"2021-02-15","arxiv_id":"2102.07412","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-acoustic-and-linguistic-embeddings","title":"Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification","date":"2021-02-15","arxiv_id":"2102.07370","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/speech-language-pre-training-for-end-to-end","slug":"speech-language-pre-training-for-end-to-end","title":"Speech-language Pre-training for End-to-end Spoken Language Understanding","date":"2021-02-11","arxiv_id":"2102.06283","repositories_listed":0,"syntology":null},{"url":null,"slug":"customizing-contextualized-language-models","title":"Customizing Contextualized Language Models forLegal Document Reviews","date":"2021-02-10","arxiv_id":"2102.05757","repositories_listed":0,"syntology":null},{"url":null,"slug":"fused-acoustic-and-text-encoding-for","title":"Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation","date":"2021-02-10","arxiv_id":"2102.05766","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-visual-reasoning-by-exploiting-the","title":"Improving Scene Graph Classification by Exploiting Knowledge from Texts","date":"2021-02-09","arxiv_id":"2102.04760","repositories_listed":0,"syntology":null},{"url":null,"slug":"newsbert-distilling-pre-trained-language","title":"NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application","date":"2021-02-09","arxiv_id":"2102.04887","repositories_listed":0,"syntology":null}],"record_sha256":"323fed3e209cf3d412ab9f7fc276c13726bea5f7bb772fdd81a09ee3f3f08256","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}