Curate and Generate: A Corpus and Method for Joint Control of Semantics and Style in Neural NLG (P19-1)
Copied to clipboard
| Challenge: | Neural natural language generation (NNLG) models generate syntactically correct utterances from structured inputs without needing hand-crafted rules or templates. |
| Approach: | They propose a method for generating a corpus of parallel meaning representations with rich style markup using freely available and naturally descriptive user reviews. |
| Outcome: | The proposed method can be scalably reused to generate NLG datasets for other domains. |
Similar Papers
Generate, Filter, and Rank: Grammaticality Classification for Production-Ready NLG Systems (N19-2)
Copied to clipboard
| Challenge: | Existing datasets for grammatical error correction don’t capture the distribution of errors that data-driven generators are likely to make. |
| Approach: | They propose a framework that allows candidates to be filtered and ranked to select the best response. |
| Outcome: | The proposed framework can be scaled with relatively low effort and achieve high precision with reasonable recall on a weather domain dataset. |
Sentence-Level Content Planning and Style Specification for Neural Text Generation (D19-1)
Copied to clipboard
| Challenge: | Recent advances in text generation systems often produce incoherent and unfaithful outputs . a novel automated text generation system takes into account content selection, text planning, and surface realization. |
| Approach: | They propose an end-to-end trained two-step text generation model that considers sentence-level content planners and language styles. |
| Outcome: | The proposed model outperforms competing models in three domains with diverse topics and varying language styles. |
Audience-Centric Natural Language Generation via Style Infusion (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to text style transfer (TST) with large volumes of parallel or non-parallel data are limiting for two reasons: it is difficult to collect large volumes and some stylistic objectives are hard to define without audience feedback. |
| Approach: | They propose a task of style infusion - infusing stylistic preferences of audiences into pretrained language generation models by leveraging pairwise human judgments to bootstrap a style analysis model and augment a seed set of judgments. |
| Outcome: | The proposed method generates compelling stylized examples with generic text prompts while balancing fluency and style adoption. |
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. |
Meaning Representations for Natural Languages: Design, Models and Applications (2024.lrec-tutorials)
Copied to clipboard
| Challenge: | a tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. |
| Approach: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. authors propose a cutting-edge, full-day tutorial for all stakeholders in the AI community. |
| Outcome: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models . it also reviews the applications of meaning representation in downstream NLP tasks and real-world applications . |
Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data (2020.coling-industry)
Copied to clipboard
Ankit Arun, Soumya Batra, Vikas Bhardwaj, Ashwini Challa, Pinar Donmez, Peyman Heidari, Hakan Inan, Shashank Jain, Anuj Kumar, Shawn Mei, Karthik Mohan, Michael White
| Challenge: | Natural language generation (NLG) is a critical component in conversational systems . Traditionally, NLG components have been deployed using template-based solutions . however, deployment of such model-based systems has been challenging due to high latency and data needs. |
| Approach: | They propose a family of techniques to deploy data-efficient neural solutions for NLG in conversational systems to production. |
| Outcome: | The proposed techniques achieve production quality with light-weight neural network models using fraction of the data needed otherwise. |
Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling (2021.eacl-main)
Copied to clipboard
| Challenge: | Neural natural language generation and understanding models are data-hungry and require massive amounts of annotated data to be competitive. |
| Approach: | They propose a framework that automatically synthesizes weak labels from large-scale weakly-labeled data with a fine-tuned GPT-2 and adapts parameter updates to the models according to the estimated label-quality. |
| Outcome: | The proposed framework outperforms benchmark systems on the E2E and Weather datasets when 100% of the training data is used. |
Back-Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations (D19-63)
Copied to clipboard
| Challenge: | Abstract Meaning Representation and Brazilian Portuguese (BP) are selected as semantic representation and language, respectively. |
| Approach: | They propose to use Brazilian Portuguese and Abstract Meaning Representation as semantic representations for NLG. |
| Outcome: | The proposed methods were evaluated on two datasets (one automatically generated and another human-generated) to compare the performance in a real context. |
The Amazing World of Neural Language Generation (2020.emnlp-tutorials)
Copied to clipboard
| Challenge: | Recent years have seen a paradigm shift in neural text generation due to advances in deep contextual language modeling and transfer learning. |
| Approach: | They will discuss how and why NLG models succeed/fail at generating coherent text. |
| Outcome: | This paper will discuss how and why these models succeed/fail at generating coherent text, and provide insights on several applications. |
A Generative Model for Joint Natural Language Understanding and Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Natural language understanding (NLU) and natural language generation (NLG) have opposite goals. |
| Approach: | They propose a generative model which couples NLU and NLG through a shared latent variable. |
| Outcome: | The proposed model achieves state-of-the-art performance on two dialogue datasets with flat and tree-structured formal representations. |