Papers with Text generation
Stylized Text Generation: Approaches and Applications (2020.acl-tutorials)
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| Challenge: | Text generation has played an important role in various applications of natural language processing. |
| Approach: | They present different settings of stylized text generation and introduce machine learning methods to represent style. |
| Outcome: | This paper presents a comprehensive literature review on stylized text generation . it focuses on the challenges and future directions of stylized generation based on machine learning . |
A Call for Clarity in Beam Search: How It Works and When It Stops (2024.lrec-main)
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| Challenge: | Empirical results show that a modified beam decoding implementation improves decoding performance of strong, neural language generation models. |
| Approach: | They propose a modification to a beam decoding implementation that generalizes the stopping criterion and provides flexibility to the depth of search. |
| Outcome: | The proposed method improves decoding performance of strong models on news text summarization and machine translation over diverse language pairs with negligible inference slowdown. |
Investigating the Effect of Relative Positional Embeddings on AMR-to-Text Generation with Structural Adapters (2023.eacl-main)
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| Challenge: | Recent approaches to text generation from Abstract Meaning Representation (AMR) have been based on neural-centered encoderdecoder architectures. |
| Approach: | They propose a structure-aware adapter which injects the input graph connectivity within PLMs using Graph Neural Networks. |
| Outcome: | The proposed adapter is robust to a variety of approaches and can be used to generate Graph-to-Text representations. |
Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints (2020.acl-main)
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| Challenge: | Existing methods for text generation ignore faithfulness between generated text and table . current methods ignore faithfulity, leading to generated information that goes beyond table content . |
| Approach: | They propose a Transformer-based generation framework to enforce faithfulness between generated text and table . they propose metric to evaluate faithfulness and automatic metric for automatic generating . |
| Outcome: | The proposed framework outperforms state-of-the-art methods in automatic evaluations and human evaluations. |
Counter-Argument Generation by Attacking Weak Premises (2021.findings-acl)
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| Challenge: | a recent work explores the generation of counter-arguments by undermining one of its premises . identifying the argument's weak premises is key to effective countering, we hypothesize . |
| Approach: | They propose a pipeline approach that first assesses the argument's weak premises and generates a counter-argument undermining the weakest among them. |
| Outcome: | The proposed approach undermins arguments by attacking weak premises . human annotators favor the proposed approach over state-of-the-art approaches . |
Pluralizing Nouns across Agglutinating Bantu Languages (C18-1)
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| Challenge: | Pluralization of nouns is a challenge for the Bantu language family . results show that the language's definition of noune classes is inadequate for computational purposes due to non-determinism in prefixes. |
| Approach: | They investigated the approach to pluralization in isiZulu and Runyankore for seven languages across three different Guthrie language zones. |
| Outcome: | The results show that the proposed pluralizers achieved over 93% accuracy and 94% accuracy on a random sample. |
Enhance Incomplete Utterance Restoration by Joint Learning Token Extraction and Text Generation (2022.naacl-main)
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| Challenge: | omitted tokens from the context contribute to incomplete utterance restoration (IUR) understanding conversational interactions through NLP has become important with increasing connectivity and range of capabilities. |
| Approach: | They propose a model for incomplete utterance restoration called JET . they construct a Picker that identifies omitted tokens and two label creation methods to support the picker. |
| Outcome: | The proposed model is better than pretrained T5 and non-generative language model methods on four benchmark datasets in extraction and abstraction scenarios. |
Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation (N19-1)
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| Challenge: | Text generation with generative adversarial networks (GANs) can be divided into text-based and code-based categories depending on the type of signals used for discrimination. |
| Approach: | They propose a text-based approach to exploit generative adversarial networks (GANs) by using autoencoders to provide a continuous representation of sentences, which they will refer to as soft-text, and hybrid latent code and text-oriented approaches with one or more discriminators. |
| Outcome: | The proposed approach outperforms the traditional GAN-based methods on two well-known datasets. |
Posterior Control of Blackbox Generation (2020.acl-main)
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| Challenge: | Existing methods for conditional natural language generation are limited in their ability to produce controlled output. |
| Approach: | They propose to augment neural generation models with discrete control states learned through a structured latent-variable approach. |
| Outcome: | The proposed approach improves over benchmarks while providing fine-grained control. |
Dialect-robust Evaluation of Generated Text (2023.acl-long)
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Jiao Sun, Thibault Sellam, Elizabeth Clark, Tu Vu, Timothy Dozat, Dan Garrette, Aditya Siddhant, Jacob Eisenstein, Sebastian Gehrmann
| Challenge: | Existing evaluation metrics that are not robust to dialect variation are difficult to measure for many groups of users and can penalize systems for producing text in lower-resource dialects. |
| Approach: | They propose a dialect-robust evaluation metric that produces the same score for system outputs that share the same semantics but are expressed in different dialects. |
| Outcome: | The proposed method significantly improves dialect robustness while preserving the correlation between automated metrics and human ratings. |
Logic-Consistency Text Generation from Semantic Parses (2021.findings-acl)
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| Challenge: | Text generation from semantic parses is challenging due to the complexity of the inner logic and the lack of automatic evaluation metrics for logic consistency. |
| Approach: | They propose a framework for logic consistent text generation from semantic parses that employs iterative training procedures and quality control. |
| Outcome: | The proposed framework enhances logic consistency and human evaluation on two benchmark datasets. |
A Topic Augmented Text Generation Model: Joint Learning of Semantics and Structural Features (D19-1)
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| Challenge: | Existing methods for text generation are limited in supervised setting and designed for specific applications. |
| Approach: | They propose a text generation model that learns semantics and structural features simultaneously . their model leverages a topic-based model to enhance the recognition of text semantics . |
| Outcome: | The proposed model outperforms state-of-the-art models in terms of text perplexity and topic coherence. |
NEUROSTRUCTURAL DECODING: Neural Text Generation with Structural Constraints (2023.acl-long)
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| Challenge: | Current approaches for conditional text generation focus on lexical constraints, but lack syntactic constraints to support complex semantic constraints. |
| Approach: | They propose a decoding algorithm that incorporates syntactic constraints to improve the quality of the generated text. |
| Outcome: | The proposed method improves on three different language generation tasks and shows improved lexical and syntactic metrics. |
BLEURT: Learning Robust Metrics for Text Generation (2020.acl-main)
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| Challenge: | Text generation has made significant advances, but evaluation metrics have lagged behind. |
| Approach: | They propose a learning evaluation metric for English based on BERT . BLEURT can model human judgment with a few thousand possibly biased training examples . |
| Outcome: | The proposed model can model human judgment with a few thousand potentially biased training examples. |
Local and Global Decoding in Text Generation (2024.findings-emnlp)
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| Challenge: | Text generation relies heavily on decoding algorithms that sample strings from a language model distribution. |
| Approach: | They propose to introduce globally-normalised versions of traditional decoding methods and propose an independent Metropolis-Hastings algorithm to approximate sampling from globally-averaged distributions without explicitly computing them. |
| Outcome: | The proposed method approximates the distributions without explicitly computing them. |