Large language models have shown impressive abilities to reason over input text, however, they are prone to hallucinations. On the other hand, end-to-end knowledge graph question answering (KGQA) models output responses grounded in facts, but they still struggle with complex reasoning, such as comparison or ordinal questions. In this paper, we propose a new method for complex question answering where we combine a knowledge graph retriever based on an end-to-end KGQA model with a language model that reasons over the retrieved facts to return an answer. We observe that augmenting language model prompts with retrieved KG facts improves performance over using a language model alone by an average of 83%. In particular, we see improvements on complex questions requiring count, intersection, or multi-hop reasoning operations.

Knowledge Graph-augmented Language Models for Complex Question Answering

We introduce Mintaka, a complex, natural, and multilingual dataset designed for experimenting with end-to-end question-answering models. Mintaka is composed of 20,000 question-answer pairs collected in English, annotated with Wikidata entities, and translated into Arabic, French, German, Hindi, Italian, Japanese, Portuguese, and Spanish for a total of 180,000 samples. Mintaka includes 8 types of complex questions, including superlative, intersection, and multi-hop questions, which were naturally elicited from crowd workers. We run baselines over Mintaka, the best of which achieves 38% hits@1 in English and 31% hits@1 multilingually, showing that existing models have room for improvement. We release Mintaka at https://github.com/amazon-research/mintaka.

Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering

Recently, end-to-end (E2E) trained models for question answering over knowledge graphs (KGQA) have delivered promising results using only a weakly supervised dataset. However, these models are trained and evaluated in a setting where hand-annotated question entities are supplied to the model, leaving the important and non-trivial task of entity resolution (ER) outside the scope of E2E learning. In this work, we extend the boundaries of E2E learning for KGQA to include the training of an ER component. Our model only needs the question text and the answer entities to train, and delivers a stand-alone QA model that does not require an additional ER component to be supplied during runtime. Our approach is fully differentiable, thanks to its reliance on a recent method for building differentiable KGs (Cohen et al., 2020). We evaluate our E2E trained model on two public datasets and show that it comes close to baseline models that use hand-annotated entities.

End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs

End-to-end question answering using a differentiable knowledge graph is a promising technique that requires only weak supervision, produces interpretable results, and is fully differentiable. Previous implementations of this technique (Cohen et al., 2020) have focused on single-entity questions using a relation following operation. In this paper, we propose a model that explicitly handles multiple-entity questions by implementing a new intersection operation, which identifies the shared elements between two sets of entities. We find that introducing intersection improves performance over a baseline model on two datasets, WebQuestionsSP (69.6% to 73.3% Hits@1) and ComplexWebQuestions (39.8% to 48.7% Hits@1), and in particular, improves performance on questions with multiple entities by over 14% on WebQuestionsSP and by 19% on ComplexWebQuestions.

Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection

With recent scaling of large pre-trained Transformer language models (LLMs), the scope of feasible NLP tasks has broadened. Significant recent work has focused on tasks that require some kind of natural language reasoning. A trajectory in question answering has led us from extraction-oriented datasets like SQuAD to “multi-hop” reasoning datasets like HotpotQA and StrategyQA. Although LLMs have shown remarkable performance on most NLP tasks, it is often unclear why their answers follow from what they know. To address this gap, a new class of explanation techniques has emerged which play an integral part in structuring the reasoning necessary to solve these datasets. For example, the chain-of-thought paradigm leverages explanations as vehicles for LLMs to mimic human reasoning processes. Entailment trees offer a way to ground multi-step reasoning in a collection of verifiable steps. Frameworks like SayCan bridge high-level planning in language and with low-level action trajectories. As a result, we see a confluence of methods blending explainable machine learning/NLP, classical AI (especially theorem proving), and cognitive science (how do humans structure explanations?). This workshop aims to bring together a diverse set of perspectives from these different traditions and attempt to establish common ground for how these various kinds of explanation structures can tackle a broad class of reasoning problems in natural language and beyond. For more details visit our workshop website: https://nl-reasoning-workshop.github.io/
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W8: The 1st Workshop on Natural Language Reasoning and Structured Explanations

workshop paper

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
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ACL 2023

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PS1 - Inference and Question Answering

poster

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

**Session Chair: **Eunsol Choi 
**In Person Volunteer:** Arman Zharmagambetov 
** Remote Volunteer:** Abhilash Nandy

5A: Question Answering 1

technical paper

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Priyanka Sen

4

13

SHORT BIO

Presentations

Knowledge Graph-augmented Language Models for Complex Question Answering

Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering

End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs

Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection

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