mlsanityData to 14 Oct 2026

Investigating Robustness of Dialog Models to Popular Figurative Language Constructs

arXiv 2110.00687 ↗First posted 1 Oct 2021cs.CLcited by 19

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Abstract (arXiv)

Humans often employ figurative language use in communication, including during interactions with dialog systems. Thus, it is important for real-world dialog systems to be able to handle popular figurative language constructs like metaphor and simile. In this work, we analyze the performance of existing dialog models in situations where the input dialog context exhibits use of figurative language. We observe large gaps in handling of figurative language when evaluating the models on two open domain dialog datasets. When faced with dialog contexts consisting of figurative language, some models show very large drops in performance compared to contexts without figurative language. We encourage future research in dialog modeling to separately analyze and report results on figurative language in order to better test model capabilities relevant to real-world use. Finally, we propose lightweight solutions to help existing models become more robust to figurative language by simply using an external resource to translate figurative language to literal (non-figurative) forms while preserving the meaning to the best extent possible.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 23 of its 39 references that are in the map. First posted on arXiv 1 Oct 2021.

Builds on

23 of 39 references.

References · 23

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
  3. 2018Personalizing Dialogue Agents: I have a dog, do you have pets too?cited by 828
  4. 2017DailyDialog: A Manually Labelled Multi-turn Dialogue Datasetcited by 652
  5. 2019COMET: Commonsense Transformers for Automatic Knowledge Graph Constructioncited by 450
  6. 2017Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoderscited by 222
  7. 2015Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Modelscited by 207
  8. 2015A Neural Conversational Modelcited by 196
  9. 2019Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attackcited by 176
  10. 2015The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systemscited by 172
  11. 2020Recipes for Safety in Open-domain Chatbotscited by 170
  12. 2020A Survey of Evaluation Metrics Used for NLG Systemscited by 164
11 more
  1. 2019Queens are Powerful too: Mitigating Gender Bias in Dialogue Generationcited by 152
  2. 2020Like hiking? You probably enjoy nature: Persona-grounded Dialog with Commonsense Expansionscited by 49
  3. 2019Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple Referencescited by 48
  4. 2019Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Studycited by 47
  5. 2020"I'd rather just go to bed": Understanding Indirect Answerscited by 36
  6. 2018Neural Metaphor Detection in Contextcited by 34
  7. 2020Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directionscited by 34
  8. 2020Probing Neural Dialog Models for Conversational Understandingcited by 10
  9. 2020Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learningcited by 6
  10. 2021Improving Automated Evaluation of Open Domain Dialog via Diverse Reference Augmentationcited by 6
  11. 2021From Solving a Problem Boldly to Cutting the Gordian Knot: Idiomatic Text Generationcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 8; 2023: 5; 2024: 3; 2025: 2; 2026: 1.

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  1. 2022FLUTE: Figurative Language Understanding through Textual Explanationscited by 79
  2. 2022Testing the Ability of Language Models to Interpret Figurative Languagecited by 67
  3. 2024Sonnet or Not, Bot? Poetry Evaluation for Large Models and Datasetscited by 18
  4. 2024A Hard Nut to Crack: Idiom Detection with Conversational Large Language Modelscited by 11
  5. 2022Are Representations Built from the Ground Up? An Empirical Examination of Local Composition in Language Modelscited by 9
  6. 2024Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Losscited by 7
  7. 2022State-of-the-art in Open-domain Conversational AI: A Surveycited by 5
  8. 2022EUREKA: EUphemism Recognition Enhanced through Knn-based methods and Augmentationcited by 5
  9. 2022Vector Representations of Idioms in Conversational Systemscited by 4
  10. 2022Getting BART to Ride the Idiomatic Train: Learning to Represent Idiomatic Expressionscited by 4
  11. 2025AI-Enabled Conversational Journaling for Advancing Parkinson's Disease Symptom Trackingcited by 4
  12. 2023Does the "most sinfully decadent cake ever" taste good? Answering Yes/No Questions from Figurative Contextscited by 2
7 more
  1. 2023Unified Representation for Non-compositional and Compositional Expressionscited by 2
  2. 2026I Came, I Saw, I Explained: Benchmarking Multimodal LLMs on Figurative Meaning in Memescited by 2
  3. 2022A Major Obstacle for NLP Research: Let's Talk about Time Allocation!cited by 1
  4. 2023Generating Continuations in Multilingual Idiomatic Contextscited by 1
  5. 2023IEKG: A Commonsense Knowledge Graph for Idiomatic Expressionscited by 1
  6. 2023Systematic word meta-sense extensioncited by 0
  7. 2025Evaluating Large Language Models on Multiword Expressions in Multilingual and Code-Switched Contextscited by 0