# E3: Entailment-driven Extracting and Editing for Conversational Machine Reading

arXiv: https://arxiv.org/abs/1906.05373 · HTML: https://mlsanity.com/paper/1906.05373/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2019.

## Built on by (most cited first, 12 of 12)

- [Natural Language Reasoning, A Survey](https://mlsanity.com/paper/2303.14725/) (2023)
- [Advances in Multi-turn Dialogue Comprehension: A Survey](https://mlsanity.com/paper/2103.03125/) (2021)
- [Open-Retrieval Conversational Machine Reading](https://mlsanity.com/paper/2102.08633/) (2021)
- [Towards Asking Clarification Questions for Information Seeking on Task-Oriented Dialogues](https://mlsanity.com/paper/2305.13690/) (2023)
- [Smoothing Dialogue States for Open Conversational Machine Reading](https://mlsanity.com/paper/2108.12599/) (2021)
- [Reasoning over Logically Interacted Conditions for Question Answering](https://mlsanity.com/paper/2205.12898/) (2022)
- [ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension](https://mlsanity.com/paper/2209.11484/) (2022)
- [Towards End-to-End Open Conversational Machine Reading](https://mlsanity.com/paper/2210.07113/) (2022)
- [Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading](https://mlsanity.com/paper/2310.13409/) (2023)
- [Unsupervised Technique To Conversational Machine Reading](https://mlsanity.com/paper/2106.15247/) (2021)
- [Advances in Multi-turn Dialogue Comprehension: A Survey](https://mlsanity.com/paper/2110.04984/) (2021)
- [Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension](https://mlsanity.com/paper/2212.09353/) (2022)

## Abstract (arXiv)

Conversational machine reading systems help users answer high-level questions (e.g. determine if they qualify for particular government benefits) when they do not know the exact rules by which the determination is made(e.g. whether they need certain income levels or veteran status). The key challenge is that these rules are only provided in the form of a procedural text (e.g. guidelines from government website) which the system must read to figure out what to ask the user. We present a new conversational machine reading model that jointly extracts a set of decision rules from the procedural text while reasoning about which are entailed by the conversational history and which still need to be edited to create questions for the user. On the recently introduced ShARC conversational machine reading dataset, our Entailment-driven Extract and Edit network (E3) achieves a new state-of-the-art, outperforming existing systems as well as a new BERT-based baseline. In addition, by explicitly highlighting which information still needs to be gathered, E3 provides a more explainable alternative to prior work. We release source code for our models and experiments at https://github.com/vzhong/e3.