{"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/text-generation/papers/39","list_of":"/task/text-generation","task":"Text Generation","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":39,"pages_in_order":54,"rows_per_page":100,"rows":[3801,3900],"of":5335,"counts":{"archive_papers_tagged":5335,"with_a_code_link":2047,"where_syntology_ran_a_sample":610,"not_listed_spam_title":0,"listed":5335,"listed_where_code_ran":610,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":503,"every_run_a_failure_of_syntologys_instrument":107,"listed_with_a_run_with_no_instrument_failure":503,"listed_every_run_a_failure_of_syntologys_instrument":107,"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/text-generation","prev":"/task/text-generation/papers/38","next":"/task/text-generation/papers/40","papers":[{"url":null,"slug":"emotion-style-transfer-with-a-specified","title":"Emotion Style Transfer with a Specified Intensity Using Deep Reinforcement Learning","date":"2021-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hitab-a-hierarchical-table-dataset-for-1","title":"HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation","date":"2021-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"how-well-do-you-know-your-audience-reader","title":"How Well Do You Know Your Audience? Toward Socially-aware Question Generation","date":"2021-10-16","arxiv_id":"2110.08445","repositories_listed":0,"syntology":null},{"url":null,"slug":"mtg-a-benchmarking-suite-for-multilingual-1","title":"MTG: A Benchmarking Suite for Multilingual Text Generation","date":"2021-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-segmentation-based-news","title":"End-to-End Segmentation-based News Summarization","date":"2021-10-15","arxiv_id":"2110.07850","repositories_listed":0,"syntology":null},{"url":null,"slug":"jurassic-is-almost-all-you-need-few-shot","title":"Jurassic is (almost) All You Need: Few-Shot Meaning-to-Text Generation for Open-Domain Dialogue","date":"2021-10-15","arxiv_id":"2110.08094","repositories_listed":0,"syntology":null},{"url":null,"slug":"finetuning-large-scale-pre-trained-language","title":"RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models","date":"2021-10-14","arxiv_id":"2110.07477","repositories_listed":0,"syntology":null},{"url":null,"slug":"hindsight-posterior-guided-training-of-1","title":"Hindsight: Posterior-guided training of retrievers for improved open-ended generation","date":"2021-10-14","arxiv_id":"2110.07752","repositories_listed":0,"syntology":null},{"url":null,"slug":"plug-tagger-a-pluggable-sequence-labeling","title":"Plug-Tagger: A Pluggable Sequence Labeling Framework Using Language Models","date":"2021-10-14","arxiv_id":"2110.07331","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-self-supervision-objectives-for-1","title":"Rethinking Self-Supervision Objectives for Generalizable Coherence Modeling","date":"2021-10-14","arxiv_id":"2110.07198","repositories_listed":0,"syntology":null},{"url":null,"slug":"bag-of-vectors-autoencoders-for-unsupervised-1","title":"Bag-of-Vectors Autoencoders for Unsupervised Conditional Text Generation","date":"2021-10-13","arxiv_id":"2110.07002","repositories_listed":0,"syntology":null},{"url":null,"slug":"clip4caption-clip-for-video-caption","title":"CLIP4Caption: CLIP for Video Caption","date":"2021-10-13","arxiv_id":"2110.06615","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-natural-language-generation-for","title":"Federated Natural Language Generation for Personalized Dialogue System","date":"2021-10-13","arxiv_id":"2110.06419","repositories_listed":0,"syntology":null},{"url":null,"slug":"smaprat-dialogpt-for-natural-language","title":"Småprat: DialoGPT for Natural Language Generation of Swedish Dialogue by Transfer Learning","date":"2021-10-12","arxiv_id":"2110.06273","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-study-on-non-autoregressive","title":"A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation","date":"2021-10-11","arxiv_id":"2110.05249","repositories_listed":0,"syntology":null},{"url":null,"slug":"viesum-how-robust-are-transformer-based","title":"VieSum: How Robust Are Transformer-based Models on Vietnamese Summarization?","date":"2021-10-08","arxiv_id":"2110.04257","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-latent-holes-of-vaes-for-text","title":"On the Latent Holes of VAEs for Text Generation","date":"2021-10-07","arxiv_id":"2110.03318","repositories_listed":0,"syntology":null},{"url":null,"slug":"cut-the-carp-fishing-for-zero-shot-story","title":"Cut the CARP: Fishing for zero-shot story evaluation","date":"2021-10-06","arxiv_id":"2110.03111","repositories_listed":0,"syntology":null},{"url":null,"slug":"simulated-annealing-for-optimization-of","title":"Simulated annealing for optimization of graphs and sequences","date":"2021-10-01","arxiv_id":"2110.01384","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-step-wise-weighting-approach-for","title":"A Step-Wise Weighting Approach for Controllable Text Generation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-training-a-simple-and-efficient","title":"Adversarial Training: A simple and efficient technique to Improving NLP Robustness","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ensembles-and-cocktails-robust-finetuning-for","title":"Ensembles and Cocktails: Robust Finetuning for Natural Language Generation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evolutionary-perspective-on-model-fine-tuning","title":"Evolutionary perspective on model fine-tuning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"gsd-generalized-stochastic-decoding","title":"GSD: Generalized Stochastic Decoding","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"not-so-fine-tuning-measures-of-common-sense","title":"Not-so fine-tuning: Measures of Common Sense for Language Models","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-reward-maximization-and-distribution","title":"On Reward Maximization and Distribution Matching for Fine-Tuning Language Models","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-latent-holes-of-vaes-for-text-1","title":"On the Latent Holes 🧀 of VAEs for Text Generation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"oumg-objective-and-universal-metric-for-text","title":"OUMG: Objective and Universal Metric for Text Generation with Guiding Ability","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"selective-token-generation-for-few-shot","title":"Selective Token Generation for Few-shot Language Modeling","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"seqpate-differentially-private-text","title":"SeqPATE: Differentially Private Text Generation via Knowledge Distillation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"text-generation-with-efficient-soft-q-1","title":"Text Generation with Efficient (Soft) $Q$-Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"topic-aware-neural-language-model-domain","title":"Topic Aware Neural Language Model: Domain Adaptation of Unconditional Text Generation Models","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"vut-versatile-ui-transformer-for-multimodal","title":"VUT: Versatile UI Transformer for Multimodal Multi-Task User Interface Modeling","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-reinforcement-learning-for-pivot","title":"Towards Reinforcement Learning for Pivot-based Neural Machine Translation with Non-autoregressive Transformer","date":"2021-09-27","arxiv_id":"2109.13097","repositories_listed":0,"syntology":null},{"url":null,"slug":"style-control-for-schema-guided-natural","title":"Style Control for Schema-Guided Natural Language Generation","date":"2021-09-24","arxiv_id":"2109.12211","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-brain-to-text-generation-neural","title":"Towards Brain-to-Text Generation: Neural Decoding with Pre-trained Encoder-Decoder Models","date":"2021-09-24","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-linguistic-knowledge-for","title":"Dependency Structure for News Document Summarization","date":"2021-09-23","arxiv_id":"2109.11199","repositories_listed":0,"syntology":null},{"url":null,"slug":"enriching-and-controlling-global-semantics","title":"Enriching and Controlling Global Semantics for Text Summarization","date":"2021-09-22","arxiv_id":"2109.10616","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-natural-language-generation-from","title":"Learning Natural Language Generation from Scratch","date":"2021-09-20","arxiv_id":"2109.09371","repositories_listed":0,"syntology":null},{"url":null,"slug":"amr-to-text-generation-with-graph-structure","title":"AMR-to-text Generation with Graph Structure Reconstruction and Coverage","date":"2021-09-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bart-light-one-decoder-layer-is-enough","title":"BART-light: One Decoder Layer Is Enough","date":"2021-09-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cdm-combining-extraction-and-generation-for-1","title":"CDM: Combining Extraction and Generation for Definition Modeling","date":"2021-09-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-generative-language-models-for","title":"Multilingual Generative Language Models for Zero-Shot Cross-Lingual Event Argument Extraction","date":"2021-09-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"relating-neural-text-degeneration-to-exposure","title":"Relating Neural Text Degeneration to Exposure Bias","date":"2021-09-17","arxiv_id":"2109.08705","repositories_listed":0,"syntology":null},{"url":null,"slug":"controllable-dialogue-generation-with","title":"Controllable Dialogue Generation with Disentangled Multi-grained Style Specification and Attribute Consistency Reward","date":"2021-09-14","arxiv_id":"2109.06717","repositories_listed":0,"syntology":null},{"url":null,"slug":"kfcnet-knowledge-filtering-and-contrastive","title":"KFCNet: Knowledge Filtering and Contrastive Learning Network for Generative Commonsense Reasoning","date":"2021-09-14","arxiv_id":"2109.06704","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-perils-of-using-mechanical-turk-to","title":"The Perils of Using Mechanical Turk to Evaluate Open-Ended Text Generation","date":"2021-09-14","arxiv_id":"2109.06835","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-argumentation-knowledge-graph","title":"End-to-end argumentation knowledge graph construction","date":"2021-09-13","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unims-a-unified-framework-for-multimodal","title":"UniMS: A Unified Framework for Multimodal Summarization with Knowledge Distillation","date":"2021-09-13","arxiv_id":"2109.05812","repositories_listed":0,"syntology":null},{"url":null,"slug":"cins-comprehensive-instruction-for-few-shot","title":"CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems","date":"2021-09-10","arxiv_id":"2109.04645","repositories_listed":0,"syntology":null},{"url":null,"slug":"controlled-neural-sentence-level-reframing-of","title":"Controlled Neural Sentence-Level Reframing of News Articles","date":"2021-09-10","arxiv_id":"2109.04957","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieve-caption-generate-visual-grounding","title":"Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models","date":"2021-09-08","arxiv_id":"2109.03892","repositories_listed":0,"syntology":null},{"url":null,"slug":"naturalness-evaluation-of-natural-language","title":"Naturalness Evaluation of Natural Language Generation in Task-oriented Dialogues using BERT","date":"2021-09-07","arxiv_id":"2109.02938","repositories_listed":0,"syntology":null},{"url":null,"slug":"rare-words-degenerate-all-words","title":"Rare Tokens Degenerate All Tokens: Improving Neural Text Generation via Adaptive Gradient Gating for Rare Token Embeddings","date":"2021-09-07","arxiv_id":"2109.03127","repositories_listed":0,"syntology":null},{"url":null,"slug":"conqx-semantic-expansion-of-spoken-queries","title":"ConQX: Semantic Expansion of Spoken Queries for Intent Detection based on Conditioned Text Generation","date":"2021-09-02","arxiv_id":"2109.00729","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-conditionality-for-natural","title":"Multimodal Conditionality for Natural Language Generation","date":"2021-09-02","arxiv_id":"2109.01229","repositories_listed":0,"syntology":null},{"url":null,"slug":"behavior-of-modern-pre-trained-language","title":"Behavior of Modern Pre-trained Language Models Using the Example of Probing Tasks","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"humor-generation-and-detection-in-code-mixed","title":"Humor Generation and Detection in Code-Mixed Hindi-English","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-abstractive-summarization-with","title":"Improving Abstractive Summarization with Commonsense Knowledge","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"models-and-tasks-for-human-centered-machine","title":"Models and Tasks for Human-Centered Machine Translation","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-reducing-repetition-in-abstractive","title":"On Reducing Repetition in Abstractive Summarization","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-prediction-in-nlp-a-survey","title":"Structured Prediction in NLP -- A survey","date":"2021-08-31","arxiv_id":"2110.02057","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-tree-decomposition-parsers-for-amr-to","title":"Latent Tree Decomposition Parsers for AMR-to-Text Generation","date":"2021-08-27","arxiv_id":"2108.12304","repositories_listed":0,"syntology":null},{"url":null,"slug":"lingxi-a-diversity-aware-chinese-modern","title":"Lingxi: A Diversity-aware Chinese Modern Poetry Generation System","date":"2021-08-27","arxiv_id":"2108.12108","repositories_listed":0,"syntology":null},{"url":null,"slug":"tree-decomposition-attention-for-amr-to-text","title":"Tree Decomposition Attention for AMR-to-Text Generation","date":"2021-08-27","arxiv_id":"2108.12300","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-the-transformer-be-used-as-a-drop-in","title":"Can the Transformer Be Used as a Drop-in Replacement for RNNs in Text-Generating GANs?","date":"2021-08-26","arxiv_id":"2108.12275","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-gan-based-models-to-sentimental","title":"Using GAN-based models to sentimental analysis on imbalanced datasets in education domain","date":"2021-08-26","arxiv_id":"2108.12061","repositories_listed":0,"syntology":null},{"url":null,"slug":"taming-the-beast-learning-to-control-neural","title":"Taming the Beast: Learning to Control Neural Conversational Models","date":"2021-08-24","arxiv_id":"2108.10561","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-bert-encoding-and-sentence-level","title":"Using BERT Encoding and Sentence-Level Language Model for Sentence Ordering","date":"2021-08-24","arxiv_id":"2108.10986","repositories_listed":0,"syntology":null},{"url":null,"slug":"cgems-a-metric-model-for-automatic-code","title":"CGEMs: A Metric Model for Automatic Code Generation using GPT-3","date":"2021-08-23","arxiv_id":"2108.10168","repositories_listed":0,"syntology":null},{"url":null,"slug":"group-based-distinctive-image-captioning-with","title":"Group-based Distinctive Image Captioning with Memory Attention","date":"2021-08-20","arxiv_id":"2108.09151","repositories_listed":0,"syntology":null},{"url":null,"slug":"ggp-a-graph-based-grouping-planner-for","title":"GGP: A Graph-based Grouping Planner for Explicit Control of Long Text Generation","date":"2021-08-18","arxiv_id":"2108.07998","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-single-example-can-improve-zero-shot-data","title":"A Single Example Can Improve Zero-Shot Data Generation","date":"2021-08-16","arxiv_id":"2108.06991","repositories_listed":0,"syntology":null},{"url":"/paper/autochart-a-dataset-for-chart-to-text","slug":"autochart-a-dataset-for-chart-to-text","title":"AutoChart: A Dataset for Chart-to-Text Generation Task","date":"2021-08-16","arxiv_id":"2108.06897","repositories_listed":0,"syntology":null},{"url":null,"slug":"reusable-templates-and-guides-for-documenting","title":"Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards","date":"2021-08-16","arxiv_id":"2108.07374","repositories_listed":0,"syntology":null},{"url":null,"slug":"sapphire-approaches-for-enhanced-concept-to","title":"SAPPHIRE: Approaches for Enhanced Concept-to-Text Generation","date":"2021-08-15","arxiv_id":"2108.06643","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-selectgen-challenge-finding-the-best","title":"The SelectGen Challenge: Finding the Best Training Samples for Few-Shot Neural Text Generation","date":"2021-08-14","arxiv_id":"2108.06614","repositories_listed":0,"syntology":null},{"url":null,"slug":"gqe-prf-generative-query-expansion-with","title":"GQE-PRF: Generative Query Expansion with Pseudo-Relevance Feedback","date":"2021-08-13","arxiv_id":"2108.06010","repositories_listed":0,"syntology":null},{"url":null,"slug":"gan-computers-generate-arts-a-survey-on","title":"GAN Computers Generate Arts? A Survey on Visual Arts, Music, and Literary Text Generation using Generative Adversarial Network","date":"2021-08-09","arxiv_id":"2108.03857","repositories_listed":0,"syntology":null},{"url":null,"slug":"intent5-search-result-diversification-using","title":"IntenT5: Search Result Diversification using Causal Language Models","date":"2021-08-09","arxiv_id":"2108.04026","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-model-evaluation-in-open-ended-text","title":"Language Model Evaluation in Open-ended Text Generation","date":"2021-08-08","arxiv_id":"2108.03578","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-semantic-regression-for-text","title":"Sentence Semantic Regression for Text Generation","date":"2021-08-06","arxiv_id":"2108.02984","repositories_listed":0,"syntology":null},{"url":null,"slug":"o2na-an-object-oriented-non-autoregressive","title":"O2NA: An Object-Oriented Non-Autoregressive Approach for Controllable Video Captioning","date":"2021-08-05","arxiv_id":"2108.02359","repositories_listed":0,"syntology":null},{"url":null,"slug":"quality-evaluation-of-the-low-resource","title":"Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text","date":"2021-08-04","arxiv_id":"2108.01861","repositories_listed":0,"syntology":null},{"url":null,"slug":"underreporting-of-errors-in-nlg-output-and","title":"Underreporting of errors in NLG output, and what to do about it","date":"2021-08-02","arxiv_id":"2108.01182","repositories_listed":0,"syntology":null},{"url":null,"slug":"all-that-s-human-is-not-gold-evaluating-human-1","title":"All That's `Human' Is Not Gold: Evaluating Human Evaluation of Generated Text","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"avoiding-overlap-in-data-augmentation-for-amr","title":"Avoiding Overlap in Data Augmentation for AMR-to-Text Generation","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"baco-a-background-knowledge-and-content-based","title":"BACO: A Background Knowledge- and Content-Based Framework for Citing Sentence Generation","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"csecu-dsg-at-semeval-2021-task-1-fusion-of","title":"CSECU-DSG at SemEval-2021 Task 1: Fusion of Transformer Models for Lexical Complexity Prediction","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"de-confounded-variational-encoder-decoder-for","title":"De-Confounded Variational Encoder-Decoder for Logical Table-to-Text Generation","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-language-generation-with-effective","title":"Enhancing Language Generation with Effective Checkpoints of Pre-trained Language Model","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-guidelines-to-deal-with-implicit","title":"Evaluation Guidelines to Deal with Implicit Phenomena to Assess Factuality in Data-to-Text Generation","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-structural-encoding-for-data-to","title":"Exploring Structural Encoding for Data-to-Text Generation","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"got-testing-for-originality-in-natural","title":"GOT: Testing for Originality in Natural Language Generation","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"human-perception-in-natural-language","title":"Human Perception in Natural Language Generation","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"i-ve-seen-things-you-people-wouldn-t-believe","title":"``I've Seen Things You People Wouldn't Believe'': Hallucinating Entities in GuessWhat?!","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"kuileixi-a-chinese-open-ended-text-adventure","title":"KuiLeiXi: a Chinese Open-Ended Text Adventure Game","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-speech-translation-from","title":"Multilingual Speech Translation from Efficient Finetuning of Pretrained Models","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nuig-dsis-submission-to-the-gem-benchmark","title":"NUIG-DSI’s submission to The GEM Benchmark 2021","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pie-a-parallel-idiomatic-expression-corpus","title":"PIE: A Parallel Idiomatic Expression Corpus for Idiomatic Sentence Generation and Paraphrasing","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"810dd7f429a053601cb556740d47006a7f908a7a820898659ea14acd4194252f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}