Soumya Batra, Shashank Jain, Peyman Heidari, Ankit Arun, Catharine Youngs, Xintong Li, Pinar Donmez, Shawn Mei, Shiunzu Kuo, Vikas Bhardwaj, Anuj Kumar, Michael White
| Challenge: | Existing approaches to generate synthetic data using simple sentence transformations and/or model-based techniques may not generate realistic error samples with respect to the NLG models. |
| Approach: | They propose a framework to train models to classify acceptability of responses generated by natural language generation models using a 2-stage approach . they use existing sentence transformations to generate samples that better resemble the output of the generation models. |
| Outcome: | The proposed approach outperforms existing techniques and can be used in few-shot settings using self-training. |
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. |
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. |
LLM Agents Implement an NLG System from Scratch: Building Interpretable Rule-Based RDF-to-Text Generators (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Existing neural approaches to generate RDF-to-text are limited in their implementation. |
| Approach: | They propose a framework where the model is “trained” through collaborative interactions among multiple LLM agents rather than traditional backpropagation. |
| Outcome: | The proposed framework reduces hallucinations and fluency penalties on the WebNLG and OpenDialKG datasets. |
Neural Mask Generator: Learning to Generate Adaptive Word Maskings for Language Model Adaptation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to train language models on diverse text corpora have brought up performance improvements on several natural language understanding (NLU) tasks. |
| Approach: | They propose a method to automatically generate domain- and task-adaptive maskings of a given text for self-supervised pre-training. |
| Outcome: | The proposed framework outperforms rule-based masking strategies on question answering and text classification datasets on which it outperformed rule-driven masking techniques. |
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. |
How to Make Neural Natural Language Generation as Reliable as Templates in Task-Oriented Dialogue (2020.emnlp-main)
Copied to clipboard
| Challenge: | Neural Natural Language Generation (NLG) systems are well known for their unreliability. |
| Approach: | They propose a data augmentation approach which restricts the output of a neural network and guarantees reliability. |
| Outcome: | The proposed approach scored 100% in semantic accuracy on the E2E NLG Challenge dataset, the same as a template system. |
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. |
Multi-task Learning for Natural Language Generation in Task-Oriented Dialogue (D19-1)
Copied to clipboard
| Challenge: | Existing methods to generate natural language for task-oriented dialogues lack naturalness and variation in language. |
| Approach: | They propose a multi-task learning framework for natural language generation that explicitly targets for naturalness in generated responses via an unconditioned language model. |
| Outcome: | The proposed framework outperforms existing models across multiple datasets in the study of natural language generation. |
Quantifying Adaptability in Pre-trained Language Models with 500 Tasks (2022.naacl-main)
Copied to clipboard
| Challenge: | a recent study examines the features and limits of LM adaptability to new tasks . many questions about the nature and limits remain unanswered . |
| Approach: | They evaluate adaptability to new tasks using a new benchmark, TaskBench500 . they find adaptation procedures differ dramatically in their ability to memorize small datasets . |
| Outcome: | The proposed benchmark compares 500 procedurally generated sequence modeling tasks to a new benchmark. |
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. |