Papers with text-to-text generation
Automatic and Human-AI Interactive Text Generation (with a focus on Text Simplification and Revision) (2024.acl-tutorials)
Copied to clipboard
| Challenge: | In this tutorial, we focus on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and then generates a revision that is improved according to some specific criteria. |
| Approach: | This tutorial focuses on text-to-text generation, a class of natural language generation tasks that takes a piece of text as input and generates a revision that is improved according to some specific criteria. |
| Outcome: | This tutorial focuses on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and generates a revision that is improved according to some specificcriteria. |
Inherent Biases in Reference-based Evaluation for Grammatical Error Correction (P18-1)
Copied to clipboard
| Challenge: | Existing evaluation systems obtain comparable or superior performance compared to humans by making few but targeted changes to the input. |
| Approach: | They propose to re-scale M 2 by the inter-annotator agreement and increase the number of references in any feasible range to overcome low coverage bias in GEC evaluation. |
| Outcome: | The proposed measure overcomes low coverage bias in GEC evaluation by re-scaling or increasing the number of references in any feasible range. |
BLEU is Not Suitable for the Evaluation of Text Simplification (D18-1)
Copied to clipboard
| Challenge: | BLEU is widely considered to be an informative metric for text-to-text generation . Xu et al. (2016) found that BLUE is not suitable for evaluation of sentence splitting . |
| Approach: | They propose to use BLEU to evaluate sentence splitting as a metric for machine translation . they propose to compare BLUE with a corpus containing multiple structural paraphrases . |
| Outcome: | The proposed BLEU is not suitable for evaluation of sentence splitting . a correlation analysis with human judgments shows low correlation with BLUE . |
Generating Syntactic Paraphrases (D18-1)
Copied to clipboard
| Challenge: | Using data-to-text generation, text-totext generation and text reduction, we show that conditioning text generation on syntactic constraints permits the generation of syntakically distinct paraphrases for the same input. |
| Approach: | They propose to use four different models for automatic generation of syntactic paraphrases to study the automatic generation process. |
| Outcome: | The proposed models can generate syntactic paraphrases for the same input and exploit different types of input to increase the number of distinct paraphrased for a given input. |
Retrieval-Augmented Controllable Review Generation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to generate reviews using attribute identifiers are limited and dependent on how well they can capture vector representations of attributes. |
| Approach: | They propose to leverage attributes as inputs for review generation by using reference sets . they propose to use these references to enrich inductive biases of given attributes . |
| Outcome: | The proposed model improves over previous approaches on automatic and human evaluation metrics. |
Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for text-to-text generation do not explicitly focus on important concepts in the input and output. |
| Approach: | They propose a framework to automatically extract, denoise, and enforce important input concepts as lexical constraints. |
| Outcome: | The proposed framework performs comparably or better than its unconstrained counterpart on automatic metrics and receives better ratings in the human evaluation. |
Time-aware Prompting for Text Generation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | a new study investigates the effects of incorporating timestamps into generation systems . textual prompts focus more on non-temporal information and are less sensitive to given timestams . |
| Approach: | They propose a data-to-text generation dataset that includes chronologically ordered revisions of biographical articles from English Wikipedia. |
| Outcome: | The proposed models improve the quality of the data-to-text generation dataset TempWikiBio . the proposed models are more sensitive to time-aware prompts than textual prompts . |
ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages (2023.findings-acl)
Copied to clipboard
| Challenge: | ERNIE-Code is a unified pre-trained language model for 116 NLs and 6 PLs. |
| Approach: | They propose a unified pre-trained language model for 116 NLs and 6 PLs . they employ span-corruption language modeling that learns patterns from monolingual NL or PL . |
| Outcome: | The proposed model outperforms previous multilingual models for NL or NL across end tasks. |