{"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/question-answering/papers/98","list_of":"/task/question-answering","task":"Question Answering","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":98,"pages_in_order":109,"rows_per_page":100,"rows":[9701,9800],"of":10817,"counts":{"archive_papers_tagged":10817,"with_a_code_link":4171,"where_syntology_ran_a_sample":1274,"not_listed_spam_title":0,"listed":10817,"listed_where_code_ran":1274,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1073,"every_run_a_failure_of_syntologys_instrument":201,"listed_with_a_run_with_no_instrument_failure":1073,"listed_every_run_a_failure_of_syntologys_instrument":201,"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/question-answering","prev":"/task/question-answering/papers/97","next":"/task/question-answering/papers/99","papers":[{"url":null,"slug":"answering-yes-no-questions-by-penalty-scoring","title":"Answering Yes-No Questions by Penalty Scoring in History Subjects of University Entrance Examinations","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"categorization-of-semantic-roles-for-1","title":"Categorization of Semantic Roles for Dictionary Definitions","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"character-aware-neural-networks-for-arabic","title":"Character-Aware Neural Networks for Arabic Named Entity Recognition for Social Media","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"chinese-tense-labelling-and-causal-analysis","title":"Chinese Tense Labelling and Causal Analysis","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cogalex-v-shared-task-cgsrc-classifying","title":"CogALex-V Shared Task: CGSRC - Classifying Semantic Relations using Convolutional Neural Networks","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-lexical-and-semantic-based-features","title":"Combining Lexical and Semantic-based Features for Answer Sentence Selection","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"compound-type-identification-in-sanskrit-what","title":"Compound Type Identification in Sanskrit: What Roles do the Corpus and Grammar Play?","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-natural-language-generation-for","title":"Context-aware Natural Language Generation for Spoken Dialogue Systems","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"context-sensitive-inference-rule-discovery-a","title":"Context-Sensitive Inference Rule Discovery: A Graph-Based Method","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dedicated-workflow-management-for-okbqa","title":"Dedicated Workflow Management for OKBQA Framework","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/dialog-based-language-learning-1","slug":"dialog-based-language-learning-1","title":"Dialog-based Language Learning","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"different-contexts-lead-to-different-word","title":"Different Contexts Lead to Different Word Embeddings","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"distributional-hypernym-generation-by-jointly","title":"Distributional Hypernym Generation by Jointly Learning Clusters and Projections","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"double-topic-shifts-in-open-domain","title":"Double Topic Shifts in Open Domain Conversations: Natural Language Interface for a Wikipedia-based Robot Application","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"eniam-categorial-syntactic-semantic-parser","title":"ENIAM: Categorial Syntactic-Semantic Parser for Polish","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"entity-supported-summarization-of-biomedical","title":"Entity-Supported Summarization of Biomedical Abstracts","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-spatial-entities-and-relations-in","title":"Extracting Spatial Entities and Relations in Korean Text","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fasthybrid-a-hybrid-model-for-efficient","title":"FastHybrid: A Hybrid Model for Efficient Answer Selection","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-rich-twitter-named-entity-recognition","title":"Feature-Rich Twitter Named Entity Recognition and Classification","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"filling-a-knowledge-graph-with-a-crowd","title":"Filling a Knowledge Graph with a Crowd","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"from-entity-linking-to-question-answering-a","title":"From Entity Linking to Question Answering -- Recent Progress on Semantic Grounding Tasks","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"global-inference-to-chinese-temporal-relation","title":"Global Inference to Chinese Temporal Relation Extraction","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hand-in-glove-deep-feature-fusion-network","title":"Hand in Glove: Deep Feature Fusion Network Architectures for Answer Quality Prediction in Community Question Answering","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/high-accuracy-rule-based-question","slug":"high-accuracy-rule-based-question","title":"High Accuracy Rule-based Question Classification using Question Syntax and Semantics","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-question-answering-over-knowledge-base","title":"Hybrid Question Answering over Knowledge Base and Free Text","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"implicit-discourse-relation-recognition-with","title":"Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improvement-of-verbnet-like-resources-by","title":"Improvement of VerbNet-like resources by frame typing","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-predictive-machine-translation","title":"Interactive-Predictive Machine Translation based on Syntactic Constraints of Prefix","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"k-srl-instance-based-learning-for-semantic","title":"K-SRL: Instance-based Learning for Semantic Role Labeling","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"label-embedding-for-zero-shot-fine-grained","title":"Label Embedding for Zero-shot Fine-grained Named Entity Typing","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"langforia-language-pipelines-for-annotating","title":"Langforia: Language Pipelines for Annotating Large Collections of Documents","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-acquisition-of-commonsense","title":"Large-Scale Acquisition of Commonsense Knowledge via a Quiz Game on a Dialogue System","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-translation-evaluation-for-arabic","title":"Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"microsyntactic-phenomena-as-a-computational","title":"Microsyntactic Phenomena as a Computational Linguistics Issue","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mongolian-named-entity-recognition-system","title":"Mongolian Named Entity Recognition System with Rich Features","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-aliasing-for-auto-generating","title":"Multilingual Aliasing for Auto-Generating Proposition Banks","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-supervision-of-semantic","title":"Multilingual Supervision of Semantic Annotation","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"n-ary-biographical-relation-extraction-using","title":"N-ary Biographical Relation Extraction using Shortest Path Dependencies","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"name-variation-in-community-question","title":"Name Variation in Community Question Answering Systems","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"natural-language-processing-for-intelligent","title":"Natural Language Processing for Intelligent Access to Scientific Information","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-attention-for-learning-to-rank","title":"Neural Attention for Learning to Rank Questions in Community Question Answering","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-clinical-paraphrase-generation-with","title":"Neural Clinical Paraphrase Generation with Attention","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nl2kb-resolving-vocabulary-gap-between","title":"NL2KB: Resolving Vocabulary Gap between Natural Language and Knowledge Base in Knowledge Base Construction and Retrieval","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"non-sentential-question-resolution-using","title":"Non-sentential Question Resolution using Sequence to Sequence Learning","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pairing-wikipedia-articles-across-languages","title":"Pairing Wikipedia Articles Across Languages","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pairwise-relation-classification-with-mirror","title":"Pairwise Relation Classification with Mirror Instances and a Combined Convolutional Neural Network","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"problematic-cases-in-the-annotation-of","title":"Problematic Cases in the Annotation of Negation in Spanish","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"proceedings-of-the-open-knowledge-base-and","title":"Proceedings of the Open Knowledge Base and Question Answering Workshop (OKBQA 2016)","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"qaf-frame-semantics-based-question","title":"QAF: Frame Semantics-based Question Interpretation","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/reading-and-thinking-re-read-lstm-unit-for","slug":"reading-and-thinking-re-read-lstm-unit-for","title":"Reading and Thinking: Re-read LSTM Unit for Textual Entailment Recognition","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-the-evaluation-for-cross-document","title":"Revisiting the Evaluation for Cross Document Event Coreference","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"selecting-sentences-versus-selecting-tree","title":"Selecting Sentences versus Selecting Tree Constituents for Automatic Question Ranking","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantically-motivated-hebrew-verb-noun-multi","title":"Semantically Motivated Hebrew Verb-Noun Multi-Word Expressions Identification","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"srdf-extracting-lexical-knowledge-graph-for","title":"SRDF: Extracting Lexical Knowledge Graph for Preserving Sentence Meaning","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"task-oriented-intrinsic-evaluation-of","title":"Task-Oriented Intrinsic Evaluation of Semantic Textual Similarity","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-development-of-multimodal-lexical","title":"The Development of Multimodal Lexical Resources","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-open-framework-for-developing-knowledge","title":"The Open Framework for Developing Knowledge Base And Question Answering System","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-building-a-domain-agnostic-natural","title":"Towards Building A Domain Agnostic Natural Language Interface to Real-World Relational Databases","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-deep-learning-in-hindi-ner-an-1","title":"Towards Deep Learning in Hindi NER: An approach to tackle the Labelled Data Sparsity","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-time-aware-knowledge-graph-completion","title":"Towards Time-Aware Knowledge Graph Completion","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"twitter-named-entity-extraction-and-linking","title":"Twitter Named Entity Extraction and Linking Using Differential Evolution","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-wikipedia-and-semantic-resources-to","title":"Using Wikipedia and Semantic Resources to Find Answer Types and Appropriate Answer Candidate Sets in Question Answering","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"vanilla-classifiers-for-distinguishing","title":"Vanilla Classifiers for Distinguishing between Similar Languages","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-question-answering-with-question","title":"Visual Question Answering with Question Representation Update (QRU)","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"whats-in-an-explanation-characterizing","title":"What's in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"wisdom-x-disaana-and-d-summ-large-scale-nlp","title":"WISDOM X, DISAANA and D-SUMM: Large-scale NLP Systems for Analyzing Textual Big Data","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"question-retrieval-for-community-based","title":"Question Retrieval for Community-based Question Answering via Heterogeneous Network Integration Learning","date":"2016-11-24","arxiv_id":"1611.08135","repositories_listed":0,"syntology":null},{"url":null,"slug":"user-personalized-satisfaction-prediction-via","title":"User Personalized Satisfaction Prediction via Multiple Instance Deep Learning","date":"2016-11-24","arxiv_id":"1611.08096","repositories_listed":0,"syntology":null},{"url":null,"slug":"answering-image-riddles-using-vision-and","title":"Answering Image Riddles using Vision and Reasoning through Probabilistic Soft Logic","date":"2016-11-17","arxiv_id":"1611.05896","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-visual-question-answering","title":"Zero-Shot Visual Question Answering","date":"2016-11-17","arxiv_id":"1611.05546","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-enhanced-hybrid-neural-network-for","title":"Knowledge Enhanced Hybrid Neural Network for Text Matching","date":"2016-11-15","arxiv_id":"1611.04684","repositories_listed":0,"syntology":null},{"url":null,"slug":"simdoc-topic-sequence-alignment-based","title":"SimDoc: Topic Sequence Alignment based Document Similarity Framework","date":"2016-11-15","arxiv_id":"1611.04822","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-video-descriptions-to-learn-video","title":"Leveraging Video Descriptions to Learn Video Question Answering","date":"2016-11-12","arxiv_id":"1611.04021","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-ibm-watson-using-automatically","title":"Training IBM Watson using Automatically Generated Question-Answer Pairs","date":"2016-11-12","arxiv_id":"1611.03932","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-attention-model-and-its-application","title":"Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering","date":"2016-11-07","arxiv_id":"1611.02266","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-question-answering-for-long","title":"Hierarchical Question Answering for Long Documents","date":"2016-11-06","arxiv_id":"1611.01839","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-wiring-question-answering-systems","title":"Self-Wiring Question Answering Systems","date":"2016-11-06","arxiv_id":"1611.01802","repositories_listed":0,"syntology":null},{"url":null,"slug":"answering-complicated-question-intents","title":"Answering Complicated Question Intents Expressed in Decomposed Question Sequences","date":"2016-11-04","arxiv_id":"1611.01242","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-stacking-gated-neural-architecture-for","title":"A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/amr-parsing-with-an-incremental-joint-model","slug":"amr-parsing-with-an-incremental-joint-model","title":"AMR Parsing with an Incremental Joint Model","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"antecedent-selection-for-sluicing-structure","title":"Antecedent Selection for Sluicing: Structure and Content","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bilingually-constrained-synthetic-data-for","title":"Bilingually-constrained Synthetic Data for Implicit Discourse Relation Recognition","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"citation-analysis-with-neural-attention","title":"Citation Analysis with Neural Attention Models","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-multiple-cues-for-visual-madlibs","title":"Combining Multiple Cues for Visual Madlibs Question Answering","date":"2016-11-01","arxiv_id":"1611.00393","repositories_listed":0,"syntology":null},{"url":"/paper/discourse-parsing-with-attention-based","slug":"discourse-parsing-with-attention-based","title":"Discourse Parsing with Attention-based Hierarchical Neural Networks","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-semantic-parsing-via-answer-type","title":"Improving Semantic Parsing via Answer Type Inference","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"it-takes-three-to-tango-triangulation","title":"It Takes Three to Tango: Triangulation Approach to Answer Ranking in Community Question Answering","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-term-embeddings-for-taxonomic","title":"Learning Term Embeddings for Taxonomic Relation Identification Using Dynamic Weighting Neural Network","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-answer-questions-from-wikipedia","title":"Learning to Answer Questions from Wikipedia Infoboxes","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mixkmeans-clustering-question-answer-archives","title":"MixKMeans: Clustering Question-Answer Archives","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nested-propositions-in-open-information","title":"Nested Propositions in Open Information Extraction","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/on-generating-characteristic-rich-question","slug":"on-generating-characteristic-rich-question","title":"On Generating Characteristic-rich Question Sets for QA Evaluation","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"paccss-it-a-parallel-corpus-of-complex-simple","title":"PaCCSS-IT: A Parallel Corpus of Complex-Simple Sentences for Automatic Text Simplification","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"poly-mining-relational-paraphrases-from","title":"POLY: Mining Relational Paraphrases from Multilingual Sentences","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"research-on-attention-memory-networks-as-a","title":"Research on attention memory networks as a model for learning natural language inference","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rule-extraction-for-tree-to-tree-transducers","title":"Rule Extraction for Tree-to-Tree Transducers by Cost Minimization","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-verbal-questions-in-iq-test-by","title":"Solving Verbal Questions in IQ Test by Knowledge-Powered Word Embedding","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-script-learning-with-recurrent","title":"Statistical Script Learning with Recurrent Neural Networks","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/supervised-distributional-hypernym-discovery","slug":"supervised-distributional-hypernym-discovery","title":"Supervised Distributional Hypernym Discovery via Domain Adaptation","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-broad-coverage-meaning-representation","title":"Towards Broad-coverage Meaning Representation: The Case of Comparison Structures","date":"2016-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"a5a516a362e1daa84b7147eb331f319095cc02dbfd1a6bfa9910f85d5d43dbe1","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}