{"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/sentence/papers/85","list_of":"/task/sentence","task":"Sentence","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":85,"pages_in_order":108,"rows_per_page":100,"rows":[8401,8500],"of":10752,"counts":{"archive_papers_tagged":10752,"with_a_code_link":3811,"where_syntology_ran_a_sample":657,"not_listed_spam_title":0,"listed":10752,"listed_where_code_ran":657,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":544,"every_run_a_failure_of_syntologys_instrument":113,"listed_with_a_run_with_no_instrument_failure":544,"listed_every_run_a_failure_of_syntologys_instrument":113,"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/sentence","prev":"/task/sentence/papers/84","next":"/task/sentence/papers/86","papers":[{"url":"/paper/reflective-decoding-network-for-image","slug":"reflective-decoding-network-for-image","title":"Reflective Decoding Network for Image Captioning","date":"2019-08-30","arxiv_id":"1908.11824","repositories_listed":0,"syntology":null},{"url":null,"slug":"memorizing-all-for-implicit-discourse","title":"Memorizing All for Implicit Discourse Relation Recognition","date":"2019-08-29","arxiv_id":"1908.11317","repositories_listed":0,"syntology":null},{"url":null,"slug":"probing-representations-learned-by-multimodal","title":"Probing Representations Learned by Multimodal Recurrent and Transformer Models","date":"2019-08-29","arxiv_id":"1908.11125","repositories_listed":0,"syntology":null},{"url":null,"slug":"classical-chinese-sentence-segmentation-for","title":"Classical Chinese Sentence Segmentation for Tomb Biographies of Tang Dynasty","date":"2019-08-28","arxiv_id":"1908.10606","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-reinforcement-learning-for-chatbots","title":"Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards","date":"2019-08-27","arxiv_id":"1908.10331","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-based-deep-reinforcement-learning","title":"Ensemble-Based Deep Reinforcement Learning for Chatbots","date":"2019-08-27","arxiv_id":"1908.10422","repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-improvement-of-a-question","title":"Incremental Improvement of a Question Answering System by Re-ranking Answer Candidates using Machine Learning","date":"2019-08-27","arxiv_id":"1908.10149","repositories_listed":0,"syntology":null},{"url":"/paper/movie-plot-analysis-via-turning-point","slug":"movie-plot-analysis-via-turning-point","title":"Movie Plot Analysis via Turning Point Identification","date":"2019-08-27","arxiv_id":"1908.10328","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-method-for-estimating-the-proximity-of","title":"A Method for Estimating the Proximity of Vector Representation Groups in Multidimensional Space. On the Example of the Paraphrase Task","date":"2019-08-25","arxiv_id":"1908.09341","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-unsupervised-image-captioning-with","title":"Towards Unsupervised Image Captioning with Shared Multimodal Embeddings","date":"2019-08-25","arxiv_id":"1908.09317","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-neural-sequence-labeling-with","title":"Position-Aware Self-Attention based Neural Sequence Labeling","date":"2019-08-24","arxiv_id":"1908.09128","repositories_listed":0,"syntology":null},{"url":null,"slug":"reference-network-for-neural-machine-1","title":"Reference Network for Neural Machine Translation","date":"2019-08-23","arxiv_id":"1908.09920","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequential-latent-spaces-for-modeling-the","title":"Sequential Latent Spaces for Modeling the Intention During Diverse Image Captioning","date":"2019-08-22","arxiv_id":"1908.08529","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-text-summarization-via-mixed","title":"Unsupervised Text Summarization via Mixed Model Back-Translation","date":"2019-08-22","arxiv_id":"1908.08566","repositories_listed":0,"syntology":null},{"url":null,"slug":"190807688","title":"Improving Neural Machine Translation with Pre-trained Representation","date":"2019-08-21","arxiv_id":"1908.07688","repositories_listed":0,"syntology":null},{"url":null,"slug":"mask-and-infill-applying-masked-language","title":"\"Mask and Infill\" : Applying Masked Language Model to Sentiment Transfer","date":"2019-08-21","arxiv_id":"1908.08039","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-contextualized-word-embeddings-in","title":"Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing -- A Tale of Two Parsers Revisited","date":"2019-08-20","arxiv_id":"1908.07397","repositories_listed":0,"syntology":null},{"url":null,"slug":"190807831","title":"Polly Want a Cracker: Analyzing Performance of Parroting on Paraphrase Generation Datasets","date":"2019-08-19","arxiv_id":"1908.07831","repositories_listed":0,"syntology":null},{"url":"/paper/align-mask-and-select-a-simple-method-for","slug":"align-mask-and-select-a-simple-method-for","title":"Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models","date":"2019-08-19","arxiv_id":"1908.06725","repositories_listed":0,"syntology":null},{"url":null,"slug":"recurrent-graph-syntax-encoder-for-neural","title":"Recurrent Graph Syntax Encoder for Neural Machine Translation","date":"2019-08-19","arxiv_id":"1908.06559","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-sensitivity-analysis-of-attention-gated","title":"A Sensitivity Analysis of Attention-Gated Convolutional Neural Networks for Sentence Classification","date":"2019-08-17","arxiv_id":"1908.06263","repositories_listed":0,"syntology":null},{"url":null,"slug":"build-it-break-it-fix-it-for-dialogue-safety","title":"Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack","date":"2019-08-17","arxiv_id":"1908.06083","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-features-matter-effective-language","title":"Language Features Matter: Effective Language Representations for Vision-Language Tasks","date":"2019-08-17","arxiv_id":"1908.06327","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-graph-distillation-for-low-resource","title":"Language Graph Distillation for Low-Resource Machine Translation","date":"2019-08-17","arxiv_id":"1908.06258","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-sentence-similarity-in-natural","title":"Leveraging Sentence Similarity in Natural Language Generation: Improving Beam Search using Range Voting","date":"2019-08-17","arxiv_id":"1908.06288","repositories_listed":0,"syntology":null},{"url":null,"slug":"hamming-sentence-embeddings-for-information","title":"Hamming Sentence Embeddings for Information Retrieval","date":"2019-08-15","arxiv_id":"1908.05541","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-self-supervised-learning-for","title":"Multi-Task Self-Supervised Learning for Disfluency Detection","date":"2019-08-15","arxiv_id":"1908.05378","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-based-automatic-post-editing-with","title":"Transformer-based Automatic Post-Editing with a Context-Aware Encoding Approach for Multi-Source Inputs","date":"2019-08-15","arxiv_id":"1908.05679","repositories_listed":0,"syntology":null},{"url":null,"slug":"unpaired-cross-lingual-image-caption","title":"Unpaired Cross-lingual Image Caption Generation with Self-Supervised Rewards","date":"2019-08-15","arxiv_id":"1908.05407","repositories_listed":0,"syntology":null},{"url":null,"slug":"xcmrc-evaluating-cross-lingual-machine","title":"XCMRC: Evaluating Cross-lingual Machine Reading Comprehension","date":"2019-08-15","arxiv_id":"1908.05416","repositories_listed":0,"syntology":null},{"url":"/paper/a-cascade-sequence-to-sequence-model-for","slug":"a-cascade-sequence-to-sequence-model-for","title":"A Cascade Sequence-to-Sequence Model for Chinese Mandarin Lip Reading","date":"2019-08-14","arxiv_id":"1908.04917","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-categorical-compositional-distributional","title":"A Categorical Compositional Distributional Modelling for the Language of Life","date":"2019-08-13","arxiv_id":"1902.09303","repositories_listed":0,"syntology":null},{"url":"/paper/structbert-incorporating-language-structures","slug":"structbert-incorporating-language-structures","title":"StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding","date":"2019-08-13","arxiv_id":"1908.04577","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-information-extraction-patterns","title":"Generating Information Extraction Patterns from Overlapping and Variable Length Annotations using Sequence Alignment","date":"2019-08-09","arxiv_id":"1908.03594","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-semantic-role-knowledge-for-relevance","title":"Using Semantic Role Knowledge for Relevance Ranking of Key Phrases inDocuments: An Unsupervised Approach","date":"2019-08-09","arxiv_id":"1908.03313","repositories_listed":0,"syntology":null},{"url":null,"slug":"scene-based-factored-attention-for-image","title":"Scene-based Factored Attention for Image Captioning","date":"2019-08-07","arxiv_id":"1908.02632","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-data-bias-problems-for-chest-x-ray","title":"Addressing Data Bias Problems for Chest X-ray Image Report Generation","date":"2019-08-06","arxiv_id":"1908.02123","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-thai-sentence-segmentation","title":"Semi-supervised Thai Sentence Segmentation Using Local and Distant Word Representations","date":"2019-08-04","arxiv_id":"1908.01294","repositories_listed":0,"syntology":null},{"url":null,"slug":"sf-net-structured-feature-network-for","title":"SF-Net: Structured Feature Network for Continuous Sign Language Recognition","date":"2019-08-04","arxiv_id":"1908.01341","repositories_listed":0,"syntology":null},{"url":null,"slug":"invariance-based-adversarial-attack-on-neural","title":"Exploring the Robustness of NMT Systems to Nonsensical Inputs","date":"2019-08-03","arxiv_id":"1908.01165","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-natural-language-based-visual-query","title":"A Natural-language-based Visual Query Approach of Uncertain Human Trajectories","date":"2019-08-01","arxiv_id":"1908.00277","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-parallel-corpus-mixtec-spanish","title":"A Parallel Corpus Mixtec-Spanish","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simple-approach-to-classify-fictional-and","title":"A Simple Approach to Classify Fictional and Non-Fictional Genres","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-test-suite-and-manual-evaluation-of-1","title":"A Test Suite and Manual Evaluation of Document-Level NMT at WMT19","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ai_blues-at-finsbd-shared-task-crf-based","title":"AI\\_Blues at FinSBD Shared Task: CRF-based Sentence Boundary Detection in PDF Noisy Text in the Financial Domain","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"aiai-at-finsbd-task-sentence-boundary","title":"aiai at FinSBD task: Sentence Boundary Detection in Noisy Texts From Financial Documents Using Deep Attention Model","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"aig-investmentsai-at-the-finsbd-task-sentence","title":"AIG Investments.AI at the FinSBD Task: Sentence Boundary Detection through Sequence Labelling and BERT Fine-tuning","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-evaluation-of-language-agnostic-inner","title":"An Evaluation of Language-Agnostic Inner-Attention-Based Representations in Machine Translation","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"analysing-representations-of-memory","title":"Analysing Representations of Memory Impairment in a Clinical Notes Classification Model","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-linguistic-complexity-and-accuracy","title":"Analyzing Linguistic Complexity and Accuracy in Academic Language Development of German across Elementary and Secondary School","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"arbdialectid-at-madar-shared-task-1-language","title":"ArbDialectID at MADAR Shared Task 1: Language Modelling and Ensemble Learning for Fine Grained Arabic Dialect Identification","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"argument-component-classification-by-relation","title":"Argument Component Classification by Relation Identification by Neural Network and TextRank","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ars_nitk-at-mediqa-2019analysing-various","title":"ARS\\_NITK at MEDIQA 2019:Analysing Various Methods for Natural Language Inference, Recognising Question Entailment and Medical Question Answering System","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-local-and-document-level-context","title":"Combining Local and Document-Level Context: The LMU Munich Neural Machine Translation System at WMT19","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"composing-a-picture-book-by-automatic-story","title":"Composing a Picture Book by Automatic Story Understanding and Visualization","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/convolutional-auto-encoding-of-sentence","slug":"convolutional-auto-encoding-of-sentence","title":"Convolutional Auto-encoding of Sentence Topics for Image Paragraph Generation","date":"2019-08-01","arxiv_id":"1908.00249","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-sentence-transformations-in-text","title":"Cross-Sentence Transformations in Text Simplification","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cuedwmt19ewclms-1","title":"CUED@WMT19:EWC\\&LMs","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"customizing-neural-machine-translation-for","title":"Customizing Neural Machine Translation for Subtitling","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"effort-aware-neural-automatic-post-editing","title":"Effort-Aware Neural Automatic Post-Editing","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ellipsis-in-chinese-amr-corpus","title":"Ellipsis in Chinese AMR Corpus","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"en-ar-bilingual-word-embeddings-without-word","title":"En-Ar Bilingual Word Embeddings without Word Alignment: Factors Effects","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-conjunction-disambiguation-on","title":"Evaluating Conjunction Disambiguation on English-to-German and French-to-German WMT 2019 Translation Hypotheses","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"filtering-of-noisy-parallel-corpora-based-on","title":"Filtering of Noisy Parallel Corpora Based on Hypothesis Generation","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"filtering-pseudo-references-by-paraphrasing","title":"Filtering Pseudo-References by Paraphrasing for Automatic Evaluation of Machine Translation","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"findings-of-the-wmt-2019-shared-task-on-1","title":"Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"findings-of-the-wmt-2019-shared-tasks-on","title":"Findings of the WMT 2019 Shared Tasks on Quality Estimation","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"grammatical-error-aware-incorrect-example","title":"Grammatical-Error-Aware Incorrect Example Retrieval System for Learners of Japanese as a Second Language","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"guided-neural-language-generation-for-2","title":"Guided Neural Language Generation for Automated Storytelling","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-deep-learning-for-arabic-dialect","title":"Hierarchical Deep Learning for Arabic Dialect Identification","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hits-sbd-at-the-finsbd-task-machine-learning","title":"HITS-SBD at the FinSBD Task: Machine Learning vs. Rule-based Sentence Boundary Detection","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-word-embeddings-using-kernel-pca","title":"Improving Word Embeddings Using Kernel PCA","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-inference-on-bilingual-parse-trees-for","title":"Joint Inference on Bilingual Parse Trees for PP-attachment Disambiguation","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"l2-processing-advantages-of-multiword","title":"L2 Processing Advantages of Multiword Sequences: Evidence from Eye-Tracking","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lium-miracl-participation-in-the-madar-arabic","title":"LIUM-MIRACL Participation in the MADAR Arabic Dialect Identification Shared Task","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mhirano-at-the-finsbd-task-pointwise","title":"mhirano at the FinSBD Task: Pointwise Prediction Based on Multi-layer Perceptron for Sentence Boundary Detection","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-five-sentence-quality","title":"Modeling Five Sentence Quality Representations by Finding Latent Spaces Produced with Deep Long Short-Memory Models","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-headed-architecture-based-on-bert-for","title":"Multi-headed Architecture Based on BERT for Grammatical Errors Correction","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-source-transformer-for-kazakh-russian","title":"Multi-Source Transformer for Kazakh-Russian-English Neural Machine Translation","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nju-submissions-for-the-wmt19-quality","title":"NJU Submissions for the WMT19 Quality Estimation Shared Task","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nuig-at-the-finsbd-task-sentence-boundary","title":"NUIG at the FinSBD Task: Sentence Boundary Detection for Noisy Financial PDFs in English and French","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"online-sentence-segmentation-for-simultaneous","title":"Online Sentence Segmentation for Simultaneous Interpretation using Multi-Shifted Recurrent Neural Network","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"parallel-corpus-filtering-based-on-fuzzy","title":"Parallel Corpus Filtering Based on Fuzzy String Matching","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"polyu_cbs-cfa-at-the-finsbd-task-sentence","title":"PolyU\\_CBS-CFA at the FinSBD Task: Sentence Boundary Detection of Financial Data with Domain Knowledge Enhancement and Bilingual Training","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"probing-word-and-sentence-embeddings-for-long","title":"Probing Word and Sentence Embeddings for Long-distance Dependencies Effects in French and English","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"proposed-taxonomy-for-gender-bias-in-text-a","title":"Proposed Taxonomy for Gender Bias in Text; A Filtering Methodology for the Gender Generalization Subtype","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"qe-bert-bilingual-bert-using-multi-task","title":"QE BERT: Bilingual BERT Using Multi-task Learning for Neural Quality Estimation","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"quality-estimation-and-translation-metrics","title":"Quality Estimation and Translation Metrics via Pre-trained Word and Sentence Embeddings","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rtm-stacking-results-for-machine-translation","title":"RTM Stacking Results for Machine Translation Performance Prediction","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"selecting-informative-context-sentence-by","title":"Selecting Informative Context Sentence by Forced Back-Translation","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-level-adaptation-for-low-resource","title":"Sentence-Level Adaptation for Low-Resource Neural Machine Translation","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"simple-but-not-naive-fine-grained-arabic","title":"Simple But Not Na\\\"\\ive: Fine-Grained Arabic Dialect Identification Using Only N-Grams","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"source-source-conditional-elmo-style-model","title":"SOURCE: SOURce-Conditional Elmo-style Model for Machine Translation Quality Estimation","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-aip-tohoku-system-at-the-bea-2019-shared","title":"The AIP-Tohoku System at the BEA-2019 Shared Task","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-finsbd-2019-shared-task-sentence-boundary","title":"The FinSBD-2019 Shared Task: Sentence Boundary Detection in PDF Noisy Text in the Financial Domain","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-romanian-corpus-annotated-with-verbal","title":"The Romanian Corpus Annotated with Verbal Multiword Expressions","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-university-of-helsinki-submission-to-the","title":"The University of Helsinki Submission to the WMT19 Parallel Corpus Filtering Task","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"thomas-the-hegemonic-osu-morphological","title":"THOMAS: The Hegemonic OSU Morphological Analyzer using Seq2seq","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tmu-transformer-system-using-bert-for-re","title":"TMU Transformer System Using BERT for Re-ranking at BEA 2019 Grammatical Error Correction on Restricted Track","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-neural-aspect-extraction-with","title":"Unsupervised Neural Aspect Extraction with Sememes","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"c5eba9f4675b9a353c65e7013d77be103703c8f21fd78d7d61acd9ce39672077","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}