{"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/169","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":169,"pages_in_order":177,"rows_per_page":100,"rows":[16801,16900],"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/168","next":"/task/language-modelling/papers/170","papers":[{"url":null,"slug":"using-related-languages-to-enhance","title":"Using Related Languages to Enhance Statistical Language Models","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uta-dlnlp-at-semeval-2016-task-1-semantic","title":"UTA DLNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uta-dlnlp-at-semeval-2016-task-12-deep","title":"UTA DLNLP at SemEval-2016 Task 12: Deep Learning Based Natural Language Processing System for Clinical Information Identification from Clinical Notes and Pathology Reports","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-api-learning","title":"Deep API Learning","date":"2016-05-27","arxiv_id":"1605.08535","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-construction-of-discourse-corpora","title":"Automatic Construction of Discourse Corpora for Dialogue Translation","date":"2016-05-22","arxiv_id":"1605.06770","repositories_listed":0,"syntology":null},{"url":null,"slug":"syntactically-guided-neural-machine","title":"Syntactically Guided Neural Machine Translation","date":"2016-05-15","arxiv_id":"1605.04569","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-analysis-of-counseling","title":"Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health","date":"2016-05-14","arxiv_id":"1605.04462","repositories_listed":0,"syntology":null},{"url":null,"slug":"noisy-parallel-approximate-decoding-for","title":"Noisy Parallel Approximate Decoding for Conditional Recurrent Language Model","date":"2016-05-12","arxiv_id":"1605.03835","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-improving-informativity-and-grammaticality","title":"On Improving Informativity and Grammaticality for Multi-Sentence Compression","date":"2016-05-07","arxiv_id":"1605.02150","repositories_listed":0,"syntology":null},{"url":null,"slug":"lstm-based-mixture-of-experts-for-knowledge","title":"LSTM-based Mixture-of-Experts for Knowledge-Aware Dialogues","date":"2016-05-05","arxiv_id":"1605.01652","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-image-captioning-by-concept-based","title":"Improving Image Captioning by Concept-based Sentence Reranking","date":"2016-05-03","arxiv_id":"1605.00855","repositories_listed":0,"syntology":null},{"url":null,"slug":"theanolm-an-extensible-toolkit-for-neural","title":"TheanoLM - An Extensible Toolkit for Neural Network Language Modeling","date":"2016-05-03","arxiv_id":"1605.00942","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-corpus-of-read-and-spontaneous-upper-saxon","title":"A Corpus of Read and Spontaneous Upper Saxon German Speech for ASR Evaluation","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cysill-ar-lein-a-corpus-of-written","title":"Cysill Ar-lein: A Corpus of Written Contemporary Welsh Compiled from an On-line Spelling and Grammar Checker","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"designing-a-speech-corpus-for-the-development","title":"Designing a Speech Corpus for the Development and Evaluation of Dictation Systems in Latvian","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-a-deterministic-shift-reduce","title":"Evaluating a Deterministic Shift-Reduce Neural Parser for Constituent Parsing","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-weighted-language-lexicons-from","title":"Extracting Weighted Language Lexicons from Wikipedia","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"languagecrawl-a-generic-tool-for-building","title":"LanguageCrawl: A Generic Tool for Building Language Models Upon Common-Crawl","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"linguistically-inspired-language-model","title":"Linguistically Inspired Language Model Augmentation for MT","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mobil-a-hybrid-feature-set-for-automatic","title":"MoBiL: A Hybrid Feature Set for Automatic Human Translation Quality Assessment","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"syntax-based-multi-system-machine-translation","title":"Syntax-based Multi-system Machine Translation","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-si-tedx-um-speech-database-a-new","title":"The SI TEDx-UM speech database: a new Slovenian Spoken Language Resource","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-based-learning-to-rank-assessment-of","title":"Transfer-Based Learning-to-Rank Assessment of Medical Term Technicality","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-a-cross-language-information-retrieval","title":"Using a Cross-Language Information Retrieval System based on OHSUMED to Evaluate the Moses and KantanMT Statistical Machine Translation Systems","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-smt-for-ocr-error-correction-of","title":"Using SMT for OCR Error Correction of Historical Texts","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"higher-order-recurrent-neural-networks","title":"Higher Order Recurrent Neural Networks","date":"2016-04-30","arxiv_id":"1605.00064","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-fifty-natural-languages-and-twelve","title":"Comparing Fifty Natural Languages and Twelve Genetic Languages Using Word Embedding Language Divergence (WELD) as a Quantitative Measure of Language Distance","date":"2016-04-28","arxiv_id":"1604.08561","repositories_listed":0,"syntology":null},{"url":"/paper/the-ibm-2016-english-conversational-telephone","slug":"the-ibm-2016-english-conversational-telephone","title":"The IBM 2016 English Conversational Telephone Speech Recognition System","date":"2016-04-27","arxiv_id":"1604.08242","repositories_listed":0,"syntology":null},{"url":null,"slug":"chinese-song-iambics-generation-with-neural","title":"Chinese Song Iambics Generation with Neural Attention-based Model","date":"2016-04-21","arxiv_id":"1604.06274","repositories_listed":0,"syntology":null},{"url":null,"slug":"ocr-error-correction-using-character","title":"OCR Error Correction Using Character Correction and Feature-Based Word Classification","date":"2016-04-21","arxiv_id":"1604.06225","repositories_listed":0,"syntology":null},{"url":null,"slug":"fully-convolutional-recurrent-network-for","title":"Fully Convolutional Recurrent Network for Handwritten Chinese Text Recognition","date":"2016-04-18","arxiv_id":"1604.04953","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-compositional-approach-to-language-modeling","title":"A Compositional Approach to Language Modeling","date":"2016-04-01","arxiv_id":"1604.00100","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-anatomy-of-a-search-and-mining-system-for","title":"The Anatomy of a Search and Mining System for Digital Archives","date":"2016-03-23","arxiv_id":"1603.07150","repositories_listed":0,"syntology":null},{"url":"/paper/semi-supervised-word-sense-disambiguation-1","slug":"semi-supervised-word-sense-disambiguation-1","title":"Semi-supervised Word Sense Disambiguation with Neural Models","date":"2016-03-22","arxiv_id":"1603.07012","repositories_listed":0,"syntology":null},{"url":null,"slug":"personalized-speech-recognition-on-mobile","title":"Personalized Speech recognition on mobile devices","date":"2016-03-10","arxiv_id":"1603.03185","repositories_listed":0,"syntology":null},{"url":null,"slug":"recursive-recurrent-nets-with-attention","title":"Recursive Recurrent Nets with Attention Modeling for OCR in the Wild","date":"2016-03-09","arxiv_id":"1603.03101","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-word-segmentation-and-lexicon","title":"Unsupervised word segmentation and lexicon discovery using acoustic word embeddings","date":"2016-03-09","arxiv_id":"1603.02845","repositories_listed":0,"syntology":null},{"url":"/paper/segmental-recurrent-neural-networks-for-end","slug":"segmental-recurrent-neural-networks-for-end","title":"Segmental Recurrent Neural Networks for End-to-end Speech Recognition","date":"2016-03-01","arxiv_id":"1603.00223","repositories_listed":0,"syntology":null},{"url":"/paper/architectural-complexity-measures-of","slug":"architectural-complexity-measures-of","title":"Architectural Complexity Measures of Recurrent Neural Networks","date":"2016-02-26","arxiv_id":"1602.08210","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-word-prediction-in-bangla-language","title":"Automated Word Prediction in Bangla Language Using Stochastic Language Models","date":"2016-02-25","arxiv_id":"1602.07803","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-transfer-with-medical-language","title":"Knowledge Transfer with Medical Language Embeddings","date":"2016-02-10","arxiv_id":"1602.03551","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-factorized-recurrent-neural-network-based","title":"A Factorized Recurrent Neural Network based architecture for medium to large vocabulary Language Modelling","date":"2016-02-04","arxiv_id":"1602.01576","repositories_listed":0,"syntology":null},{"url":null,"slug":"smoothing-parameter-estimation-framework-for","title":"Smoothing parameter estimation framework for IBM word alignment models","date":"2016-01-14","arxiv_id":"1601.03650","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-entropy-a-new-evaluation-metric","title":"Contrastive Entropy: A new evaluation metric for unnormalized language models","date":"2016-01-03","arxiv_id":"1601.00248","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-contextual-language-model-to-improve","title":"A Contextual Language Model to Improve Machine Translation of Pronouns by Re-ranking Translation Hypotheses","date":"2016-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-phrase-based-smt-using-cross","title":"Improving Phrase-Based SMT Using Cross-Granularity Embedding Similarity","date":"2016-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"re-assessing-the-impact-of-smt-techniques","title":"Re-assessing the Impact of SMT Techniques with Human Evaluation: a Case Study on English---Croatian","date":"2016-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-non-negative-matrix-language-modeling","title":"Sparse Non-negative Matrix Language Modeling","date":"2016-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"feedforward-sequential-memory-networks-a-new","title":"Feedforward Sequential Memory Networks: A New Structure to Learn Long-term Dependency","date":"2015-12-28","arxiv_id":"1512.08301","repositories_listed":0,"syntology":null},{"url":null,"slug":"backward-and-forward-language-modeling-for","title":"Backward and Forward Language Modeling for Constrained Sentence Generation","date":"2015-12-21","arxiv_id":"1512.06612","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-2015-sheffield-system-for-transcription","title":"The 2015 Sheffield System for Transcription of Multi-Genre Broadcast Media","date":"2015-12-21","arxiv_id":"1512.06643","repositories_listed":0,"syntology":null},{"url":null,"slug":"pjait-systems-for-the-iwslt-2015-evaluation","title":"PJAIT Systems for the IWSLT 2015 Evaluation Campaign Enhanced by Comparable Corpora","date":"2015-12-05","arxiv_id":"1512.01639","repositories_listed":0,"syntology":null},{"url":null,"slug":"fixed-point-performance-analysis-of-recurrent","title":"Fixed-Point Performance Analysis of Recurrent Neural Networks","date":"2015-12-04","arxiv_id":"1512.01322","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-semi-supervised-dialog-act-tagging-for","title":"A Semi Supervised Dialog Act Tagging for Telugu","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-word-embeddings-and-sequence","title":"Analysis of Word Embeddings and Sequence Features for Clinical Information Extraction","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"applying-sanskrit-concepts-for-reordering-in","title":"Applying Sanskrit Concepts for Reordering in MT","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/deep-visual-analogy-making","slug":"deep-visual-analogy-making","title":"Deep Visual Analogy-Making","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"how-few-is-too-few-determining-the-minimum","title":"How few is too few? Determining the minimum acceptable number of LSA dimensions to visualise text cohesion with Lex","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lstm-neural-reordering-feature-for","title":"LSTM Neural Reordering Feature for Statistical Machine Translation","date":"2015-12-01","arxiv_id":"1512.00177","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequence-to-sequence-video-to-text-1","title":"Sequence to Sequence - Video to Text","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"spoken-language-translation-for-polish","title":"Spoken Language Translation for Polish","date":"2015-11-24","arxiv_id":"1511.07788","repositories_listed":0,"syntology":null},{"url":null,"slug":"stories-in-the-eye-contextual-visual","title":"Stories in the Eye: Contextual Visual Interactions for Efficient Video to Language Translation","date":"2015-11-20","arxiv_id":"1511.06674","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-articulated-motion-models-from","title":"Learning Articulated Motion Models from Visual and Lingual Signals","date":"2015-11-17","arxiv_id":"1511.05526","repositories_listed":0,"syntology":null},{"url":null,"slug":"larger-context-language-modelling","title":"Larger-Context Language Modelling","date":"2015-11-11","arxiv_id":"1511.03729","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-language-modeling-on-cnn-image","title":"Visual Language Modeling on CNN Image Representations","date":"2015-11-09","arxiv_id":"1511.02872","repositories_listed":0,"syntology":null},{"url":null,"slug":"multinomial-loss-on-held-out-data-for-the","title":"Multinomial Loss on Held-out Data for the Sparse Non-negative Matrix Language Model","date":"2015-11-05","arxiv_id":"1511.01574","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaqn-an-adaptive-quasi-newton-algorithm-for","title":"adaQN: An Adaptive Quasi-Newton Algorithm for Training RNNs","date":"2015-11-04","arxiv_id":"1511.01169","repositories_listed":0,"syntology":null},{"url":null,"slug":"mining-local-gazetteers-of-literary-chinese","title":"Mining Local Gazetteers of Literary Chinese with CRF and Pattern based Methods for Biographical Information in Chinese History","date":"2015-11-04","arxiv_id":"1511.01556","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-with-intention-for-a-neural-network","title":"Attention with Intention for a Neural Network Conversation Model","date":"2015-10-29","arxiv_id":"1510.08565","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-language-model-based-approach-towards-large","title":"A language model based approach towards large scale and lightweight language identification systems","date":"2015-10-13","arxiv_id":"1510.03602","repositories_listed":0,"syntology":null},{"url":null,"slug":"feedforward-sequential-memory-neural-networks","title":"Feedforward Sequential Memory Neural Networks without Recurrent Feedback","date":"2015-10-09","arxiv_id":"1510.02693","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-segmentation","title":"Language Segmentation","date":"2015-10-06","arxiv_id":"1510.01717","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameterized-neural-network-language-models","title":"Parameterized Neural Network Language Models for Information Retrieval","date":"2015-10-06","arxiv_id":"1510.01562","repositories_listed":0,"syntology":null},{"url":"/paper/senticap-generating-image-descriptions-with","slug":"senticap-generating-image-descriptions-with","title":"SentiCap: Generating Image Descriptions with Sentiments","date":"2015-10-06","arxiv_id":"1510.01431","repositories_listed":0,"syntology":null},{"url":null,"slug":"batch-normalized-recurrent-neural-networks","title":"Batch Normalized Recurrent Neural Networks","date":"2015-10-05","arxiv_id":"1510.01378","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-machine-learning-method-to-distinguish","title":"A Machine Learning Method to Distinguish Machine Translation from Human Translation","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"acquiring-distributed-representations-for","title":"Acquiring distributed representations for verb-object pairs by using word2vec","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-improved-hierarchical-word-sequence","title":"An Improved Hierarchical Word Sequence Language Model Using Directional Information","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-learners-writing-fluency-based-on","title":"以語言模型判斷學習者文句流暢度(Analyzing Learners `Writing Fluency Based on Language Model)[In Chinese]","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"construction-of-semantic-collocation-bank","title":"Construction of Semantic Collocation Bank Based on Semantic Dependency Parsing","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"distant-supervised-language-model-for","title":"Distant-supervised Language Model for Detecting Emotional Upsurge on Twitter","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"english-to-chinese-translation-how-chinese","title":"English to Chinese Translation: How Chinese Character Matters","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-word-embedding-and-concept","title":"使用詞向量表示與概念資訊於中文大詞彙連續語音辨識之語言模型調適(Exploring Word Embedding and Concept Information for Language Model Adaptation in Mandarin Large Vocabulary Continuous Speech Recognition) [In Chinese]","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-dictionary-construction-via-pivot","title":"Large-scale Dictionary Construction via Pivot-based Statistical Machine Translation with Significance Pruning and Neural Network Features","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-translation-experiments-on-padic-a","title":"Machine Translation Experiments on PADIC: A Parallel Arabic DIalect Corpus","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-popularity-of-machine-generated","title":"Measuring Popularity of Machine-Generated Sentences Using Term Count, Document Frequency, and Dependency Language Model","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"naver-machine-translation-system-for-wat-2015","title":"NAVER Machine Translation System for WAT 2015","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-language-model-for-chinese","title":"Neural Network Language Model for Chinese Pinyin Input Method Engine","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"system-combination-of-rbmt-plus-spe-and","title":"System Combination of RBMT plus SPE and Preordering plus SMT","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"toshiba-mt-system-description-for-the-wat2015","title":"Toshiba MT System Description for the WAT2015 Workshop","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-algorithmic-discovery-of-biographical","title":"Toward Algorithmic Discovery of Biographical Information in Local Gazetteers of Ancient China","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-statistical-speech-translation","title":"Real-Time Statistical Speech Translation","date":"2015-09-30","arxiv_id":"1509.09090","repositories_listed":0,"syntology":null},{"url":null,"slug":"noise-robust-asr-for-the-third-chime","title":"Noise-Robust ASR for the third 'CHiME' Challenge Exploiting Time-Frequency Masking based Multi-Channel Speech Enhancement and Recurrent Neural Network","date":"2015-09-24","arxiv_id":"1509.07211","repositories_listed":0,"syntology":null},{"url":null,"slug":"noise-robust-ioacas-speech-separation-and","title":"Noise Robust IOA/CAS Speech Separation and Recognition System For The Third 'CHIME' Challenge","date":"2015-09-21","arxiv_id":"1509.06103","repositories_listed":0,"syntology":null},{"url":null,"slug":"telugu-ocr-framework-using-deep-learning","title":"Telugu OCR Framework using Deep Learning","date":"2015-09-20","arxiv_id":"1509.05962","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-discovery-of-linguistic","title":"Unsupervised Discovery of Linguistic Structure Including Two-level Acoustic Patterns Using Three Cascaded Stages of Iterative Optimization","date":"2015-09-07","arxiv_id":"1509.02208","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-binarized-neural-network-joint-model-for","title":"A Binarized Neural Network Joint Model for Machine Translation","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-coarse-grained-model-for-optimal-coupling","title":"A Coarse-Grained Model for Optimal Coupling of ASR and SMT Systems for Speech Translation","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-between-count-and-neural-network","title":"A Comparison between Count and Neural Network Models Based on Joint Translation and Reordering Sequences","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-discriminative-training-procedure-for","title":"A Discriminative Training Procedure for Continuous Translation Models","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"7363f775aff7101a4f569cef7dba5136752f53483e6ddeb069302a69a5d70daa","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}