Papers with Natural language generation
Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data (2020.coling-industry)
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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. |
Natural Language Generation by Hierarchical Decoding with Linguistic Patterns (N18-2)
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| Challenge: | a common and mostly adopted method is the rule-based (or template-based) method for natural language generation. |
| Approach: | They propose a hierarchical decoding NLG model based on linguistic patterns in different levels. |
| Outcome: | The proposed method outperforms the traditional one with a smaller model size. |
A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation (N18-1)
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| Challenge: | a recent study has shown that natural language generators produce utterances with humanlike coherence and naturalness for many different kinds of content. |
| Approach: | They propose to use a neural language generator to generate a syntactically and semantically correct utterance from a given MR. |
| Outcome: | The proposed model outperforms state-of-the-art models on restaurant, TV and laptop datasets. |
Quantifying Bias from Decoding Techniques in Natural Language Generation (2022.coling-1)
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| Challenge: | Natural language generation (NLG) models can propagate social bias towards particular demography. |
| Approach: | They propose to examine whether bias metrics like toxicity and sentiment are impacted by decoding techniques that use stochastic decoding. |
| Outcome: | The proposed methods reveal the imperative of testing inference time bias and provide evidence on the usefulness of inspecting the entire decoding spectrum. |
Coupling Context Modeling with Zero Pronoun Recovering for Document-Level Natural Language Generation (2021.emnlp-main)
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| Challenge: | ZP-annotated natural language generation (NLG) corpora are scarce in pro-drop languages . despite efforts to bridge the discrepancy between human and machine, zero pronouns still persist in pro -drop tasks. |
| Approach: | They propose a highly adaptive two-stage approach to couple context modeling with ZP recovering to mitigate the ZP problem in NLG tasks. |
| Outcome: | The proposed approach can improve translation, question answering, and summarization tasks. |
Continual Learning for Natural Language Generation in Task-oriented Dialog Systems (2020.findings-emnlp)
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| Challenge: | Existing neural approaches for natural language generation are typically developed offline for specific domains. |
| Approach: | They propose a method to expand NLG knowledge incrementally to new domains . major challenge is catastrophic forgetting, meaning a model forgets the knowledge it has learned before . |
| Outcome: | The proposed method outperforms other methods by effectively mitigating catastrophic forgetting issue. |
Dual Inference for Improving Language Understanding and Generation (2020.findings-emnlp)
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| Challenge: | Existing studies have exploited the duality of the task pairs in machine translation and speech recognition. |
| Approach: | They propose to leverage the duality in the inference stage without retraining whole models. |
| Outcome: | The proposed method is effective in both NLU and NLG tasks, providing the great potential of practical use. |
Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation (2021.emnlp-main)
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| Challenge: | Natural language generation (NLG) tasks have complex nature and require manual evaluation. |
| Approach: | They propose a unifying perspective based on the nature of information change in NLG tasks . they propose 'information alignment' metrics that can be used to evaluate different aspects of NLG . |
| Outcome: | The proposed metrics achieve stronger or comparable correlations with human judgement compared to state-of-the-art metrics in diverse tasks. |
IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation (2021.emnlp-main)
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Samuel Cahyawijaya, Genta Indra Winata, Bryan Wilie, Karissa Vincentio, Xiaohong Li, Adhiguna Kuncoro, Sebastian Ruder, Zhi Yuan Lim, Syafri Bahar, Masayu Khodra, Ayu Purwarianti, Pascale Fung
| Challenge: | Lack of publicly available NLG benchmarks for low-resource languages poses a challenge . authors show that IndoBART and IndoGPT achieve competitive performance on all tasks . |
| Approach: | They propose a benchmark to measure natural language generation progress in three low-resource languages of Indonesia . they use a corpus of pretraining datasets to build their models . |
| Outcome: | The proposed benchmark measures progress in Indonesian, Javanese, and Sundanese . the results highlight the importance of pretraining on closely related, localized languages . |
Logic-Guided Message Generation from Raw Real-Time Sensor Data (2022.lrec-1)
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| Challenge: | Developing a natural language generation model to enable human pilots to communicate with drones is challenging because of its redundant nature and diversity. |
| Approach: | They propose a corpus for a specific domain that instantiates these properties by combining sensor data with text. |
| Outcome: | The proposed model can alert the human pilot of the system state and environment in preparation of handover of control. |