Rationale-based Approaches
Wenting Zhao
https://wenting-zhao.github.io/

Part 7/7 of the ACL 2023 Tutorial
Complex Reasoning in Natural Language

What are rationales?

Definition of rationales●Rationales are extractive texts that significantly influence what the output
would be.

●Rationales were first introduced in Zaidan et al. (2007)

Input

What do people do during their time off from work?
(A) take trips, (B) become hysterical

Output(A) take trips

Rationale models may be Supervised / Unsupervised●Zaidan et al. (2007) supervise models with rationales●Lei et al. (2016) proposed self-rationalizing models without rationale
supervision, making producing rationales possible for every dataset

Rationale models may be Faithful / Unfaithful●

Rationale models are faithful if they predict outputs given only the rationales●

Rationale models need to be faithful to be deemed as an explainable modelRationale models may extract Tokens / Sentences●

Tokens for short inputs●

Sentences / paragraphs for long inputs●Complex reasoning tasks often consist of long inputs, i.e., many (andpotentially very long) documents

Rationale models may be Single-hop / Structured●Single-hop rationale models predict sentences in a rationale independently●Structured rationale models explicitly consider sentence structures

Rationale models are closely related to Retrieval●Documents to be retrieved can be seen as rationales●

Better rationale models can lead to better retrieval models●More retrieval work is covered in another ACL tutorial: Retrieval-based
Language Models and Applications (2pm in the afternoon)[Lewis et al., 2020]

What are benefits and costs of rationale models?

Benefits of supervised rationale models●

Rationale models can improve task performance[Yang et al., 2018]

Benefits of supervised rationale models●

Rationale models are robust to adversarial attacks

w/o rationale supervision

w/ rationale supervision[Chen et al., 2022]

Costs of supervising rationales●

Only 29 datasets have annotated rationales [Wiegreffe and Marasović, 2022]●

Rationale annotations are expensive to collect [Geva et al., 2021]●

Rationales can be subjective to annotate [Zhang et al., 2020]

Benefits and costs of structured rationale models●Necessary to get reasoning correct for problems involve compositionalstructures●

However, there may be training and / or inference overhead

Benefits of faithful rationale models●

Faithful rationale models allow users to evaluate the trustworthiness of theirpredictions

Input

What do people do during their time off from work?
(A) take trips, (B) become hysterical

Output(A) take trips[Rajani et al., 2019]

Benefits of faithful rationale models●

Faithful rationale models allow users to debug datasets[Zhao et al., 2023]

Costs of faithful rationale models●Potentially more computationally expensive to train●May not necessarily improve task accuracyOverview of methods

unsupervised

structured

faithfulTransformer models that handle long inputs●Feed the entire input into transformer models that handle long-form texts anddirectly predict a rationale from contextualized embeddings of [SPC] tokens●

Input: {question} [SPC] sent 1 [SPC] … [SPC] sent n●

Predict on [SPC] tokens for whether a sentence is included

InputOutput{question} [SPC] sent 1 [SPC] … [SPC] sent n0 / 1?

0 / 1?

0 / 1?

[Beltagy et al., 2020, Zaheer et al., 2020]

Handling long inputs with regular transformers●

First, a document selection module filters
out answer-unrelated documents●

Then, an answer and explain module,
trained with a multi-task loss, jointly
predicts an answer and a rationale[Tu et al., 2019]

Utilizing graph neural networks (GNNs)●Use graph neural networks to capture the relationship between different hops●

Graphs are often built with entities[Thayaparan et al., 2019; Fang et al., 2020; Qiu et al., 2019]

Graph vs. No graph

AnswerRationale

Joint[Fang et al., 2020]

Overview of methods

unsupervised

structured

faithfulPrediction confidence●Treat which part leads to highest prediction confidence as rationale[Glockner et al., 2020; Atanasova, et al., 2023]

Latent rationales●Models a single document as a latent variable●

Easy to build: This model is on HuggingFace

Input

Rationale

Output[Lewis et al., 2020]

Overview of methods

unsupervised

structured

faithfulLatent set rationales●

Explicitly models multi-hop
reasoning as set-prediction
problems[Zhao et al., 2023]

Modeling documents sets vs. single documents●

HUG: models interdependency between documents and sentences●

HUG-ind: models documents and sentences independently

Comparison-based
reasoning

Bridge-based
reasoning

Conclusions & directions for rationale-based approaches●

There are options for the specific use scenarios●

Rationale selection doesn’t automatically solved by larger, better language
models, due to long input lengths●

How to scale up unsupervised rationale selection to open-domain setting?

Graph rationales[Trivedi et al., 2022]

Conclusions & directions for rationale-based approaches●

There are options for the specific use scenarios●

Rationale selection doesn’t automatically solved by larger, better language
models, due to long input lengths●

How to scale up unsupervised rationale selection to open-domain setting?

●

Is it possible to learn rationale graphs?

Conclusion for the tutorial●

Complex reasoning tasks still remains unsolved even with LLMs●

Making reasoning explicit is a promising direction to build NLP systems thatgeneralize and can be trusted by users●Some of the explicit reasoning systems are easy to implement with
open-source tools --- start building today!

Paper list
[Rajani et al., 2019] Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019. Explain Yourself! Leveraging
Language Models for Commonsense Reasoning. In Proceedings of the 57th Annual Meeting of the Association for ComputationalLinguistics, pages 4932–4942, Florence, Italy. Association for Computational Linguistics.
[Trivedi et al., 2022] Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022. ♫ MuSiQue: MultihopQuestions via Single-hop Question Composition. Transactions of the Association for Computational Linguistics, 10:539–554.
[Yang et al., 2018] Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D.
Manning. 2018. HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering. In Proceedings of the 2018 Conferenceon Empirical Methods in Natural Language Processing, pages 2369–2380, Brussels, Belgium. Association for Computational
Linguistics.
[Chen et al., 2022] Howard Chen, Jacqueline He, Karthik Narasimhan, and Danqi Chen. 2022. Can Rationalization Improve
Robustness?. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics:
Human Language Technologies, pages 3792–3805, Seattle, United States. Association for Computational Linguistics.
Max Glockner, Ivan Habernal, and Iryna Gurevych. 2020. Why do you think that? Exploring Faithful Sentence-Level Rationales Without
Supervision. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1080–1095, Online. Association for
Computational Linguistics.

Paper list
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016. Rationalizing Neural Predictions. In Proceedings of the 2016Conference on Empirical Methods in Natural Language Processing, pages 107–117, Austin, Texas. Association for
Computational Linguistics.
[Thayaparan et al., 2019] Mokanarangan Thayaparan, Marco Valentino, Viktor Schlegel, and André Freitas. 2019. Identifying
Supporting Facts for Multi-hop Question Answering with Document Graph Networks. In Proceedings of the ThirteenthWorkshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13), pages 42–51, Hong Kong.
Association for Computational Linguistics.
[Fang et al., 2020] Yuwei Fang, Siqi Sun, Zhe Gan, Rohit Pillai, Shuohang Wang, and Jingjing Liu. 2020. Hierarchical Graph
Network for Multi-hop Question Answering. In Proceedings of the 2020 Conference on Empirical Methods in NaturalLanguage Processing (EMNLP), pages 8823–8838, Online. Association for Computational Linguistics.
[Qiu et al., 2019] Lin Qiu, Yunxuan Xiao, Yanru Qu, Hao Zhou, Lei Li, Weinan Zhang, and Yong Yu. 2019. Dynamically Fused
Graph Network for Multi-hop Reasoning. In Proceedings of the 57th Annual Meeting of the Association for Computational
Linguistics, pages 6140–6150, Florence, Italy. Association for Computational Linguistics.
[Lewis et al., 2020] Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.T.,
Rocktäschel, T. and Riedel, S., 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in
Neural Information Processing Systems, 33, pp.9459-9474.