{"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/139","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":139,"pages_in_order":177,"rows_per_page":100,"rows":[13801,13900],"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/138","next":"/task/language-modelling/papers/140","papers":[{"url":null,"slug":"anna-enhanced-language-representation-for-2","title":"ANNA”:\" Enhanced Language Representation for Question Answering","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"challenges-in-including-extra-linguistic","title":"Challenges in including extra-linguistic context in pre-trained language models","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-extraction-and-generation-for","title":"Combining Extraction and Generation for Constructing Belief-Consequence Causal Links","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"continuing-pre-trained-model-with-multiple","title":"Continuing Pre-trained Model with Multiple Training Strategies for Emotional Classification","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"controlled-text-generation-using-dictionary","title":"Controlled Text Generation Using Dictionary Prior in Variational Autoencoders","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cue-bot-a-conversational-agent-for-assistive","title":"Cue-bot: A Conversational Agent for Assistive Technology","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cuebot-cue-controlled-response-generation-for","title":"CueBot: Cue-Controlled Response Generation for Assistive Interaction Usages","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dataset-debt-in-biomedical-language-modeling","title":"Dataset Debt in Biomedical Language Modeling","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"design-principles-of-an-open-source-language","title":"Design principles of an open-source language modeling microservice package for AAC text-entry applications","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-specific-knowledge-distillation-yields","title":"Domain-specific knowledge distillation yields smaller and better models for conversational commerce","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"eico-improving-few-shot-text-classification","title":"EICO: Improving Few-Shot Text Classification via Explicit and Implicit Consistency Regularization","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-language-model-prompts-using","title":"Exploiting Language Model Prompts Using Similarity Measures: A Case Study on the Word-in-Context Task","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-person-names-from-user-generated","title":"Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-combined-coreference-resolution-methods","title":"Graph-combined Coreference Resolution Methods on Conversational Machine Reading Comprehension with Pre-trained Language Model","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-controllable-text-generation-with-1","title":"Improving Controllable Text Generation with Position-Aware Weighted Decoding","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-multiple-documents-grounded-goal","title":"Improving Multiple Documents Grounded Goal-Oriented Dialog Systems via Diverse Knowledge Enhanced Pretrained Language Model","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"is-whole-word-masking-always-better-for-2","title":"“Is Whole Word Masking Always Better for Chinese BERT?”: Probing on Chinese Grammatical Error Correction","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"kiqa-knowledge-infused-question-answering","title":"KIQA: Knowledge-Infused Question Answering Model for Financial Table-Text Data","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-multi-document-summarization-with","title":"Large-Scale Multi-Document Summarization with Information Extraction and Compression","date":"2022-05-01","arxiv_id":"2205.00548","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-similar-users-for-personalized","title":"Leveraging Similar Users for Personalized Language Modeling with Limited Data","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mr-p-a-parallel-decoding-algorithm-for","title":"MR-P: A Parallel Decoding Algorithm for Iterative Refinement Non-Autoregressive Translation","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mtl-slt-multi-task-learning-for-spoken","title":"MTL-SLT: Multi-Task Learning for Spoken Language Tasks","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"p-tuning-prompt-tuning-can-be-comparable-to","title":"P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"phrase-aware-unsupervised-constituency-1","title":"Phrase-aware Unsupervised Constituency Parsing","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"query-generation-with-external-knowledge-for","title":"Query Generation with External Knowledge for Dense Retrieval","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ssn-armm-lt-edi-acl2022-hope-speech-detection","title":"SSN_ARMM@ LT-EDI -ACL2022: Hope Speech Detection for Equality, Diversity, and Inclusion Using ALBERT model","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"syntax-guided-contrastive-learning-for-pre","title":"Syntax-guided Contrastive Learning for Pre-trained Language Model","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tagging-without-rewriting-a-probabilistic","title":"Tagging Without Rewriting: A Probabilistic Model for Unpaired Sentiment and Style Transfer","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-best-of-both-worlds-dual-channel-language","title":"The Best of both Worlds: Dual Channel Language modeling for Hope Speech Detection in low-resourced Kannada","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-xiaomi-text-to-text-simultaneous-speech","title":"The Xiaomi Text-to-Text Simultaneous Speech Translation System for IWSLT 2022","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-berts-mood-the-role-of","title":"Understanding BERT’s Mood: The Role of Contextual-Embeddings as User-Representations for Depression Assessment","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-asr-generated-text-for-spoken-language","title":"Using ASR-Generated Text for Spoken Language Modeling","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-cross-lingual-part-of-speech-tagging","title":"Using Cross-Lingual Part of Speech Tagging for Partially Reconstructing the Classic Language Family Tree Model","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-neural-topic-models-to-track-context","title":"Using neural topic models to track context shifts of words: a case study of COVID-related terms before and after the lockdown in April 2020","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"what-works-and-doesnt-work-a-deep-decoder-for","title":"What Works and Doesn’t Work, A Deep Decoder for Neural Machine Translation","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"you-reap-what-you-sow-on-the-challenges-of","title":"You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-initial-look-at-self-reprogramming","title":"Self-Programming Artificial Intelligence Using Code-Generating Language Models","date":"2022-04-30","arxiv_id":"2205.00167","repositories_listed":0,"syntology":null},{"url":null,"slug":"layoutbert-masked-language-layout-model-for","title":"LayoutBERT: Masked Language Layout Model for Object Insertion","date":"2022-04-30","arxiv_id":"2205.00347","repositories_listed":0,"syntology":null},{"url":null,"slug":"to-interpolate-or-not-to-interpolate-prf","title":"To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers","date":"2022-04-30","arxiv_id":"2205.00235","repositories_listed":0,"syntology":null},{"url":null,"slug":"visualizing-and-explaining-language-models","title":"Visualizing and Explaining Language Models","date":"2022-04-30","arxiv_id":"2205.10238","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-natural-language-feedback","title":"Training Language Models with Language Feedback","date":"2022-04-29","arxiv_id":"2204.14146","repositories_listed":0,"syntology":null},{"url":null,"slug":"pyramidclip-hierarchical-feature-alignment","title":"PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining","date":"2022-04-29","arxiv_id":"2204.14095","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-effect-of-pretraining-corpora-on-in","title":"On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Model","date":"2022-04-28","arxiv_id":"2204.13509","repositories_listed":0,"syntology":null},{"url":null,"slug":"rigoberta-a-state-of-the-art-language-model","title":"RigoBERTa: A State-of-the-Art Language Model For Spanish","date":"2022-04-27","arxiv_id":"2205.10233","repositories_listed":0,"syntology":null},{"url":null,"slug":"parkinson-s-disease-diagnostics-using-ai-and","title":"Parkinson's disease diagnostics using AI and natural language knowledge transfer","date":"2022-04-26","arxiv_id":"2204.12559","repositories_listed":0,"syntology":null},{"url":null,"slug":"pretraining-chinese-bert-for-detecting-word","title":"Pretraining Chinese BERT for Detecting Word Insertion and Deletion Errors","date":"2022-04-26","arxiv_id":"2204.12052","repositories_listed":0,"syntology":null},{"url":null,"slug":"c3-continued-pretraining-with-contrastive","title":"C3: Continued Pretraining with Contrastive Weak Supervision for Cross Language Ad-Hoc Retrieval","date":"2022-04-25","arxiv_id":"2204.11989","repositories_listed":0,"syntology":null},{"url":null,"slug":"crystal-transformer-self-learning-neural","title":"Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials","date":"2022-04-25","arxiv_id":"2204.11953","repositories_listed":0,"syntology":null},{"url":null,"slug":"ed2lm-encoder-decoder-to-language-model-for-1","title":"ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference","date":"2022-04-25","arxiv_id":"2204.11458","repositories_listed":0,"syntology":null},{"url":null,"slug":"super-prompting-utilizing-model-independent","title":"Super-Prompting: Utilizing Model-Independent Contextual Data to Reduce Data Annotation Required in Visual Commonsense Tasks","date":"2022-04-25","arxiv_id":"2204.11922","repositories_listed":0,"syntology":null},{"url":null,"slug":"balancing-expert-utilization-in-mixture-of","title":"Sparsely-gated Mixture-of-Expert Layers for CNN Interpretability","date":"2022-04-22","arxiv_id":"2204.10598","repositories_listed":0,"syntology":null},{"url":null,"slug":"locally-aggregated-feature-attribution-on","title":"Locally Aggregated Feature Attribution on Natural Language Model Understanding","date":"2022-04-22","arxiv_id":"2204.10893","repositories_listed":0,"syntology":null},{"url":null,"slug":"taygete-at-semeval-2022-task-4-roberta-based","title":"Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language","date":"2022-04-22","arxiv_id":"2204.10519","repositories_listed":0,"syntology":null},{"url":null,"slug":"wabert-a-low-resource-end-to-end-model-for","title":"WaBERT: A Low-resource End-to-end Model for Spoken Language Understanding and Speech-to-BERT Alignment","date":"2022-04-22","arxiv_id":"2204.10461","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-unintended-memorization-in-language","title":"Detecting Unintended Memorization in Language-Model-Fused ASR","date":"2022-04-20","arxiv_id":"2204.09606","repositories_listed":0,"syntology":null},{"url":null,"slug":"decbert-enhancing-the-language-understanding","title":"DecBERT: Enhancing the Language Understanding of BERT with Causal Attention Masks","date":"2022-04-19","arxiv_id":"2204.08688","repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-syntax-aware-language-modeling-1","title":"Multilingual Syntax-aware Language Modeling through Dependency Tree Conversion","date":"2022-04-19","arxiv_id":"2204.08644","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-study-on-prompt-based-few-shot-learning","title":"A Study on Prompt-based Few-Shot Learning Methods for Belief State Tracking in Task-oriented Dialog Systems","date":"2022-04-18","arxiv_id":"2204.08167","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-language-modeling-for-goal-1","title":"Context-Aware Language Modeling for Goal-Oriented Dialogue Systems","date":"2022-04-18","arxiv_id":"2204.10198","repositories_listed":0,"syntology":null},{"url":null,"slug":"umass-pcl-at-semeval-2022-task-4-pre-trained","title":"UMass PCL at SemEval-2022 Task 4: Pre-trained Language Model Ensembles for Detecting Patronizing and Condescending Language","date":"2022-04-18","arxiv_id":"2204.08304","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-entity-and-tweet-characterization","title":"Zero-shot Entity and Tweet Characterization with Designed Conditional Prompts and Contexts","date":"2022-04-18","arxiv_id":"2204.08405","repositories_listed":0,"syntology":null},{"url":null,"slug":"simplebert-a-pre-trained-model-that-learns-to","title":"SimpleBERT: A Pre-trained Model That Learns to Generate Simple Words","date":"2022-04-16","arxiv_id":"2204.07779","repositories_listed":0,"syntology":null},{"url":null,"slug":"wordalchemy-a-transformer-based-reverse","title":"WordAlchemy: A transformer-based Reverse Dictionary","date":"2022-04-16","arxiv_id":"2204.10181","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-surprisal-in-issue-trackers-actionable","title":"Is Surprisal in Issue Trackers Actionable?","date":"2022-04-15","arxiv_id":"2204.07363","repositories_listed":0,"syntology":null},{"url":null,"slug":"rows-from-many-sources-enriching-row","title":"Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model","date":"2022-04-14","arxiv_id":"2204.07014","repositories_listed":0,"syntology":null},{"url":null,"slug":"curriculum-a-broad-coverage-benchmark-for-1","title":"Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding","date":"2022-04-13","arxiv_id":"2204.06283","repositories_listed":0,"syntology":null},{"url":null,"slug":"hit-at-semeval-2022-task-2-pre-trained","title":"HIT at SemEval-2022 Task 2: Pre-trained Language Model for Idioms Detection","date":"2022-04-13","arxiv_id":"2204.06145","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-not-fire-the-linguist-grammatical-profiles","title":"Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Change","date":"2022-04-12","arxiv_id":"2204.05717","repositories_listed":0,"syntology":null},{"url":null,"slug":"mining-logical-event-schemas-from-pre-trained","title":"Mining Logical Event Schemas From Pre-Trained Language Models","date":"2022-04-12","arxiv_id":"2204.05939","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-bigscience-multilingual-model-to","title":"Adapting BigScience Multilingual Model to Unseen Languages","date":"2022-04-11","arxiv_id":"2204.04873","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-the-gap-between-language-models-and","title":"Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling","date":"2022-04-11","arxiv_id":"2204.05210","repositories_listed":0,"syntology":null},{"url":null,"slug":"breaking-character-are-subwords-good-enough-1","title":"Breaking Character: Are Subwords Good Enough for MRLs After All?","date":"2022-04-10","arxiv_id":"2204.04748","repositories_listed":0,"syntology":null},{"url":null,"slug":"pushing-on-personality-detection-from-verbal","title":"Pushing on Personality Detection from Verbal Behavior: A Transformer Meets Text Contours of Psycholinguistic Features","date":"2022-04-10","arxiv_id":"2204.04629","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarking-for-public-health-surveillance","title":"Benchmarking for Public Health Surveillance tasks on Social Media with a Domain-Specific Pretrained Language Model","date":"2022-04-09","arxiv_id":"2204.04521","repositories_listed":0,"syntology":null},{"url":null,"slug":"idpg-an-instance-dependent-prompt-generation-1","title":"IDPG: An Instance-Dependent Prompt Generation Method","date":"2022-04-09","arxiv_id":"2204.04497","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-semi-supervised-learning-of-automatic","title":"Advancing Semi-Supervised Learning for Automatic Post-Editing: Data-Synthesis by Mask-Infilling with Erroneous Terms","date":"2022-04-08","arxiv_id":"2204.03896","repositories_listed":0,"syntology":null},{"url":null,"slug":"autoencoding-language-model-based-ensemble","title":"Autoencoding Language Model Based Ensemble Learning for Commonsense Validation and Explanation","date":"2022-04-07","arxiv_id":"2204.03324","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-wordnet-construction-using-word","title":"Towards Automatic Construction of Filipino WordNet: Word Sense Induction and Synset Induction Using Sentence Embeddings","date":"2022-04-07","arxiv_id":"2204.03251","repositories_listed":0,"syntology":null},{"url":null,"slug":"maestro-matched-speech-text-representations","title":"MAESTRO: Matched Speech Text Representations through Modality Matching","date":"2022-04-07","arxiv_id":"2204.03409","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-complementary-joint-training-approach-using","title":"A Complementary Joint Training Approach Using Unpaired Speech and Text for Low-Resource Automatic Speech Recognition","date":"2022-04-05","arxiv_id":"2204.02023","repositories_listed":0,"syntology":null},{"url":null,"slug":"lamner-code-comment-generation-using","title":"LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition","date":"2022-04-05","arxiv_id":"2204.09654","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-effectiveness-of-pretrained-models-for","title":"On the Effectiveness of Pretrained Models for API Learning","date":"2022-04-05","arxiv_id":"2204.03498","repositories_listed":0,"syntology":null},{"url":null,"slug":"aligned-weight-regularizers-for-pruning-1","title":"Aligned Weight Regularizers for Pruning Pretrained Neural Networks","date":"2022-04-04","arxiv_id":"2204.01385","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-joint-speech-text-embeddings-for","title":"An Analysis of Semantically-Aligned Speech-Text Embeddings","date":"2022-04-04","arxiv_id":"2204.01235","repositories_listed":0,"syntology":null},{"url":null,"slug":"into-tts-intonation-template-based-prosody","title":"Into-TTS : Intonation Template Based Prosody Control System","date":"2022-04-04","arxiv_id":"2204.01271","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-dialect-density-estimation-for","title":"Automatic Dialect Density Estimation for African American English","date":"2022-04-03","arxiv_id":"2204.00967","repositories_listed":0,"syntology":null},{"url":null,"slug":"effect-and-analysis-of-large-scale-language","title":"Effect and Analysis of Large-scale Language Model Rescoring on Competitive ASR Systems","date":"2022-04-01","arxiv_id":"2204.00212","repositories_listed":0,"syntology":null},{"url":null,"slug":"nc-dre-leveraging-non-entity-clue-information","title":"NC-DRE: Leveraging Non-entity Clue Information for Document-level Relation Extraction","date":"2022-04-01","arxiv_id":"2204.00255","repositories_listed":0,"syntology":null},{"url":null,"slug":"syntax-informed-question-answering-with","title":"Syntax-informed Question Answering with Heterogeneous Graph Transformer","date":"2022-04-01","arxiv_id":"2204.09655","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-cross-lingual-aphasia-detection","title":"Zero-Shot Cross-lingual Aphasia Detection using Automatic Speech Recognition","date":"2022-04-01","arxiv_id":"2204.00448","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-23-mw-data-centre-is-all-you-need","title":"A 23 MW data centre is all you need","date":"2022-03-31","arxiv_id":"2203.17265","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-study-of-language-model","title":"An Empirical Study of Language Model Integration for Transducer based Speech Recognition","date":"2022-03-31","arxiv_id":"2203.16776","repositories_listed":0,"syntology":null},{"url":null,"slug":"esgbert-language-model-to-help-with","title":"ESGBERT: Language Model to Help with Classification Tasks Related to Companies Environmental, Social, and Governance Practices","date":"2022-03-31","arxiv_id":"2203.16788","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-pre-trained-transformers-for","title":"Generative Pre-Trained Transformers for Biologically Inspired Design","date":"2022-03-31","arxiv_id":"2204.09714","repositories_listed":0,"syntology":null},{"url":null,"slug":"scaling-language-model-size-in-cross-device","title":"Scaling Language Model Size in Cross-Device Federated Learning","date":"2022-03-31","arxiv_id":"2204.09715","repositories_listed":0,"syntology":null},{"url":null,"slug":"auto-mlm-improved-contrastive-learning-for","title":"Auto-MLM: Improved Contrastive Learning for Self-supervised Multi-lingual Knowledge Retrieval","date":"2022-03-30","arxiv_id":"2203.16187","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-spoken-dialogue-language-modeling","title":"Generative Spoken Dialogue Language Modeling","date":"2022-03-30","arxiv_id":"2203.16502","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-speech-recognition-for-indic","title":"Improving Speech Recognition for Indic Languages using Language Model","date":"2022-03-30","arxiv_id":"2203.16595","repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-dynamic-semantics-into-pre","title":"Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis","date":"2022-03-30","arxiv_id":"2203.16369","repositories_listed":0,"syntology":null},{"url":null,"slug":"position-based-prompting-for-health-outcome","title":"Position-based Prompting for Health Outcome Generation","date":"2022-03-30","arxiv_id":"2204.03489","repositories_listed":0,"syntology":null}],"record_sha256":"daf08bf89a50fb8fff8352f046d616d1a41e79e1e4ded2055b3039a6cf40f3de","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}