It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions --- a paradigm known as case-based reasoning (CBR). We propose a neuro-symbolic CBR approach (\alg) for question answering over large knowledge bases. CBR-KBQA consists of a nonparametric memory that stores cases (question and logical forms) and a parametric model that can generate a logical form for a new question by retrieving cases that are relevant to it. On several KBQA datasets that contain complex questions, CBR-KBQA achieves competitive performance. For example, on the CWQ dataset, CBR-KBQA outperforms the current state of the art by 11\% on accuracy. Furthermore, we show that CBR-KBQA is capable of using new cases \emph{without} any further training: by incorporating a few human-labeled examples in the case memory, CBR-KBQA is able to successfully generate logical forms containing unseen KB entities as well as relations.

Case-based Reasoning for Natural Language Queries over Knowledge Bases

Pre-trained language models have emerged as highly successful methods for learning good text representations. However, the amount of structured knowledge retained in such models, and how (if at all) it can be extracted, remains an open question. In this work, we aim at directly learning text representations that leverage structured knowledge about entities mentioned in the text. This can be particularly beneficial for downstream tasks which are knowledge-intensive. Our approach utilizes self-attention between words in the text and knowledge graph (KG) entities mentioned in the text. While existing methods require entity-linked data for pre-training, we train using a mention-span masking objective and a candidate ranking objective -- which doesn't require any entity-links and only assumes access to an alias table for retrieving candidates, enabling large-scale pre-training. We show that the proposed model learns knowledge-informed text representations that yield improvements on the downstream tasks over existing methods.

Simultaneously Self-Attending to Text and Entities forKnowledge-Informed Text Representations

Existing work on tabular representationlearning jointly models tables and associated text using self-supervised objective functions derived from pretrained language models such as BERT. While this joint pretraining improves tasks involving paired tables and text (e.g., answering questions about tables), we show that it underperforms on tasks that operate over tables without any associated text (e.g., populating missing cells). We devise a simple pretraining objective (corrupt cell detection) that learns exclusively from tabular data and reaches the state-of-the-art on a suite of tablebased prediction tasks. Unlike competing approaches, our model (TABBIE) provides embeddings of all table substructures (cells, rows,and columns), and it also requires far less compute to train. A qualitative analysis of our model’s learned cell, column, and row representations shows that it understands complex table semantics and numerical trends.


TABBIE: Pretrained Representations of Tabular Data

Virtual Poster Session II: Question Answering

poster

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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.


<i>**Session Chair: **Jonathan Berant</i><br><br>
<i>**In Person Volunteer:** Dheeraj Mekala</i><br>
<i>** Remote Volunteer:** Chenglei Si</i>

8B: Question Answering 3

technical paper

6th Workshop on Representation Learning for NLP (RepL4NLP-2021)

workshop paper

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IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).



<i>**Session Chair: ** Alan Ritter </i><br><br>

10B-Oral: Information Extraction

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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Dung Thai

4

18

SHORT BIO

Presentations

Case-based Reasoning for Natural Language Queries over Knowledge Bases

Case-based Reasoning for Natural Language Queries over Knowledge Bases

Simultaneously Self-Attending to Text and Entities forKnowledge-Informed Text Representations

TABBIE: Pretrained Representations of Tabular Data

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