Challenge: Generating natural language under complex constraints is a principled formulation towards controllable text generation.
Approach: They propose a method to specify combinatorial constraints for sentence generation . they use a tree search algorithm embedded into the proposal process of the Markov Chain Monte Carlo .
Outcome: The proposed method achieves consistent and significant improvement on multiple language generation tasks.

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Controlled Language Generation for Language Learning Items (2022.emnlp-industry)

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Challenge: Recent advances in pre-trained language models have resulted in success in generating fluent English text.
Approach: They propose to employ natural language generation to rapidly generate English language items . they experiment with deep pretrained models and develop methods for controlling items for factors relevant in language learning .
Outcome: The proposed framework shows high grammatically scores for all models and higher complexity over baseline models.
Generating Natural Anagrams: Towards Language Generation Under Hard Combinatorial Constraints (D19-1)

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Challenge: Existing methods for creating anagrams do not pay much attention to the naturalness of the generated anagramms.
Approach: They propose to combine depth-first search with modern neural language models to generate anagrams by permutation of characters in an input sentence or phrase.
Outcome: The proposed method generates significantly more natural anagrams than baseline methods.
PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decoding (2022.naacl-main)

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Challenge: Large language models (LM) based on transformers generate plausible long texts . a discriminator-guided approach allows to apply constraints more finely and dynamically.
Approach: They propose to use a discriminator-guided approach to generate constrained texts without fine-tuning the LM.
Outcome: The proposed method is easier and cheaper to train than fine-tuning the LM.
Generating Text from Language Models (2023.acl-tutorials)

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Challenge: a growing percentage of natural language processing tasks focus on the generation of text from probabilistic language models.
Approach: They will provide a centralized discussion of critical considerations when choosing how to generate from a language model.
Outcome: This tutorial will provide a centralized discussion of critical considerations when choosing how to generate from a language model.
Unlocking Anticipatory Text Generation: A Constrained Approach for Large Language Models Decoding (2024.emnlp-main)

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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.
Gradient-based Constrained Sampling from Language Models (2022.emnlp-main)

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Challenge: Large pretrained language models are successful at generating fluent text but are notoriously hard to controllably sample from.
Approach: They propose a sampling procedure that combines the log-likelihood of the language model with arbitrary constraints in a single energy function and then generates samples in . non-autoregressive manner.
Outcome: The proposed method improves on text generation with soft and hard constraints and keyword-guided generation.
Syntax-Guided Controlled Generation of Paraphrases (2020.tacl-1)

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Challenge: Recent work has explored the incorporation of complex syntactic-guidance as constraints in the task of controlled text generation.
Approach: They propose an end-to-end framework for controlled paraphrase generation that incorporates complex syntactic-guidance constraints into the task.
Outcome: The proposed framework generates syntax-conforming sentences while not compromising on relevance.
Knowledge-Enriched Natural Language Generation (2021.emnlp-tutorials)

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Challenge: Knowledge-enriched text generation poses unique challenges in modeling and learning . a roadmap will outline the state-of-the-art methods to tackle these challenges .
Approach: They propose a roadmap to tackle the challenges of knowledge-enriched text generation . they will dive deep into various technical components to illustrate how to represent knowledge .
Outcome: This tutorial outlines the state-of-the-art methods to tackle the problem . it aims to show how to represent knowledge, feed knowledge into a generation model, evaluate results .
NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints (2021.naacl-main)

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Challenge: Conditional text generation often requires lexical constraints, i.e., which words should or shouldn't be included in the output text.
Approach: They propose an algorithm that enables neural language models to generate fluent text while satisfying complex lexical constraints.
Outcome: The proposed algorithm outperforms existing methods on four benchmarks and shows that it handles any set of lexical constraints expressible under predicate logic while its asymptotic runtime is equivalent to conventional beam search.
Natural Language Generation: Recently Learned Lessons, Directions for Semantic Representation-based Approaches, and the Case of Brazilian Portuguese Language (P19-2)

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Challenge: Natural Language Generation (NLG) is a promising area in Natural Language Processing (NLP) .
Approach: They present a review of the literature on Natural Language Generation in Brazilian Portuguese.
Outcome: The proposed approaches are based on the Abstract Meaning Representation formalism and have potential future directions.

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