Improving Adversarial Text Generation by Modeling the Distant Future (2020.acl-main)
Copied to clipboard
Ruiyi Zhang, Changyou Chen, Zhe Gan, Wenlin Wang, Dinghan Shen, Guoyin Wang, Zheng Wen, Lawrence Carin
| Challenge: | Recent work has shown excellent performance on text generation tasks by combining reinforcement learning (RL) and generative models. |
| Approach: | They propose a model-based imitation-learning approach to improve text generation performance by focusing on a long horizon. |
| Outcome: | The proposed model improves on a number of text-generation tasks and provides intermediate rewards for generator optimization. |
Similar Papers
Diffusion Guided Language Modeling (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing guidance methods for text generation are prone to decoding errors and degrade performance. |
| Approach: | They propose a model that steers an auto-regressive language model to generate text with desired properties. |
| Outcome: | The proposed model outperforms existing guidance methods on a wide range of benchmark data sets. |
Plan-then-Generate: Controlled Data-to-Text Generation via Planning (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies focus on producing results that are close to the references, i.e. what to generate and in what order (the output structure) cannot be explicitly controlled by the users. |
| Approach: | They propose a Plan-then-Generate framework to improve the controllability of neural data-to-text models. |
| Outcome: | The proposed model can control both the intra-sentence and inter-sentent structure of the generated output. |
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. |
Unlocking Anticipatory Text Generation: A Constrained Approach for Large Language Models Decoding (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models have shown a powerful ability for text generation, but undesired behaviors such as toxicity and hallucinations can manifest. |
| Approach: | They propose to formalize text generation as a future-constrained generation problem to minimize undesirable behaviors and enforce faithfulness to instructions. |
| Outcome: | The proposed approach is effective across three tasks, including keyword-constrained generation, toxicity reduction, and factual correctness in question-answering. |
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. |
Data-to-text Generation with Variational Sequential Planning (2022.tacl-1)
Copied to clipboard
| Challenge: | Recent advances in data-to-text generation have greatly facilitated the task of generating textual output from non-linguistic input. |
| Approach: | They propose a neural model enhanced with a planning component responsible for organizing high-level information in a coherent and meaningful way. |
| Outcome: | The proposed model outperforms baseline models and is sample-efficient in the face of limited training data. |
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing adversarial text generation approaches can lead to generation lacking diversity or fluency, whereas perturbing in the intermediate representation space can lead a model to generate generations that are not related to the input. |
| Approach: | They propose to generate adversarial texts through controllable attributes that are known to be invariant to task labels. |
| Outcome: | The proposed model generates more diverse and fluent adversarial examples, compared to existing approaches, and is more robust against model re-training and different model architectures. |
Data-to-text Generation with Macro Planning (2021.tacl-1)
Copied to clipboard
| Challenge: | Recent approaches to data-to-text generation adopt the encoder-decoder architecture . however, these models perform poorly at selecting appropriate content and ordering it coherently . |
| Approach: | They propose a neural model with a macro planning stage followed by a generation stage . they use data from databases of records, simulations of physical systems, accounting spreadsheets . |
| Outcome: | The proposed model outperforms baselines on two data-to-text benchmarks . it uses the encoderdecoder architecture and is compared with existing models . |
Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents (D19-56)
Copied to clipboard
| Challenge: | Data availability is a bottleneck during early stages of development of new capabilities for intelligent artificial agents. |
| Approach: | They propose to use conditional variational auto-encoders to augment training data of a popular commercial artificial agent with a small set of phrase templates to generate new semantically similar phrases. |
| Outcome: | The proposed approach outperforms the previous controlled text generation techniques with limited data and significantly outperformed the previous methods. |
PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for text generation still suffer from incoherence problems . Neural sequence-to-sequence (seq2sequ) models generate fluent results . |
| Approach: | They propose a novel generation framework that leverages autoregressive self-attention mechanism to conduct content planning and surface realization dynamically. |
| Outcome: | The proposed framework outperforms baseline models and generates more coherent texts with richer contents. |