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Found 25 papers, 19 papers with code
BERTering RAMS: What and How Much does BERT Already Know About Event Arguments? - A Study on the RAMS Dataset
Next, we find that linear combinations of these heads, estimated with approx.
DYAD: A Descriptive Yet Abjuring Density efficient approximation to linear neural network layers
DYAD is based on a bespoke near-sparse matrix structure which approximates the dense "weight" matrix W that matrix-multiplies the input in the typical realization of such a layer, a. k. a DENSE.
EUREKA: EUphemism Recognition Enhanced through Knn-based methods and Augmentation
We introduce EUREKA, an ensemble-based approach for performing automatic euphemism detection.
PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation
To this end, we propose PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation.
PANCETTA: Phoneme Aware Neural Completion to Elicit Tongue Twisters Automatically
Through automatic and human evaluation, as well as qualitative analysis, we show that PANCETTA generates novel, phonetically difficult, fluent, and semantically meaningful tongue twisters.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
Investigating Robustness of Dialog Models to Popular Figurative Language Constructs
Humans often employ figurative language use in communication, including during interactions with dialog systems.
Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data
Existing text classification methods mainly focus on a fixed label set, whereas many real-world applications require extending to new fine-grained classes as the number of samples per label increases.
Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models
We investigate the use of multimodal information contained in images as an effective method for enhancing the commonsense of Transformer models for text generation.
SAPPHIRE: Approaches for Enhanced Concept-to-Text Generation
We motivate and propose a suite of simple but effective improvements for concept-to-text generation called SAPPHIRE: Set Augmentation and Post-hoc PHrase Infilling and REcombination.
Automatic Construction of Evaluation Suites for Natural Language Generation Datasets
By applying this framework to the GEM generation benchmark, we propose an evaluation suite made of 80 challenge sets, demonstrate the kinds of analyses that it enables and shed light onto the limits of current generation models.
Improving Automated Evaluation of Open Domain Dialog via Diverse Reference Augmentation
Multiple different responses are often plausible for a given open domain dialog context.
A Survey of Data Augmentation Approaches for NLP
In this paper, we present a comprehensive and unifying survey of data augmentation for NLP by summarizing the literature in a structured manner.
NAREOR: The Narrative Reordering Problem
In this paper, we propose and investigate the task of Narrative Reordering (NAREOR) which involves rewriting a given story in a different narrative order while preserving its plot.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
Ranked #1 on
Extreme Summarization
on GEM-XSum
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
BERTering RAMS: What and How Much does BERT Already Know About Event Arguments? -- A Study on the RAMS Dataset
Next, we find that linear combinations of these heads, estimated with approx 11% of available total event argument detection supervision, can push performance well-higher for some roles - highest two being Victim (68. 29% Accuracy) and Artifact(58. 82% Accuracy).
GenAug: Data Augmentation for Finetuning Text Generators
We also examine the relationship between the amount of augmentation and the quality of the generated text.
SCDE: Sentence Cloze Dataset with High Quality Distractors From Examinations
We introduce SCDE, a dataset to evaluate the performance of computational models through sentence prediction.
Ranked #3 on
Question Answering
on SCDE
Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented Dialog
We are hitherto the first to investigate the use of generative classifiers for OOD detection at test-time.
(Male, Bachelor) and (Female, Ph.D) have different connotations: Parallelly Annotated Stylistic Language Dataset with Multiple Personas
Stylistic variation in text needs to be studied with different aspects including the writer's personal traits, interpersonal relations, rhetoric, and more.
Learning to Generate Move-by-Move Commentary for Chess Games from Large-Scale Social Forum Data
This paper examines the problem of generating natural language descriptions of chess games.
Shakespearizing Modern Language Using Copy-Enriched Sequence to Sequence Models
Variations in writing styles are commonly used to adapt the content to a specific context, audience, or purpose.
Detecting and Explaining Causes From Text For a Time Series Event
Our quantitative and human analysis show empirical evidence that our method successfully extracts meaningful causality relationships between time series with textual features and generates appropriate explanation between them.
Shakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence Models
Variations in writing styles are commonly used to adapt the content to a specific context, audience, or purpose.
CharManteau: Character Embedding Models For Portmanteau Creation
Portmanteaus are a word formation phenomenon where two words are combined to form a new word.
