Self-Supervised Knowledge Triplet Learning for Zero-Shot Question Answering (2020.emnlp-main)
Copied to clipboard
| Challenge: | Current supervised Question Answering methods rely on expensive data annotations and can introduce unintended annotator bias. |
| Approach: | They propose a self-supervised task over knowledge graphs that can be supervised by a data annotation tool. |
| Outcome: | The proposed task performs better than pre-trained language models on a large dataset. |
Similar Papers
Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types (N18-1)
Copied to clipboard
| Challenge: | Existing factoid question answering systems rely on annotated datasets such as SimpleQuestions to generate questions from knowledge graphs. |
| Approach: | They propose a neural model that generates questions from knowledge graphs triples in a “zero-shot” setup. |
| Outcome: | The proposed model outperforms state-of-the-art on this task. |
Graph Reasoning for Question Answering with Triplet Retrieval (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to answer complex questions require reasoning over knowledge graphs (KGs) state-of-the-art methods constrain retrieved knowledge in local subgraphs and discard more diverse triplets that are disconnected but useful for question answering. |
| Approach: | They propose a method to retrieve the most relevant triplets from KGs and then rerank them, which are then concatenated with questions to be fed into language models. |
| Outcome: | The proposed method outperforms state-of-the-art methods on commonsenseQA and OpenbookQA datasets with 4.6% absolute accuracy. |
Complex Question Answering on knowledge graphs using machine translation and multi-task learning (2021.eacl-main)
Copied to clipboard
Saurabh Srivastava, Mayur Patidar, Sudip Chowdhury, Puneet Agarwal, Indrajit Bhattacharya, Gautam Shroff
| Challenge: | Existing approaches to question answering on knowledge graphs are based on a modularized sequential approach where errors in one module lead to the accumulation of errors in downstream modules. |
| Approach: | They propose a multi-task BERT based Neural Machine Translation model to address these challenges. |
| Outcome: | The proposed model can answer questions over a knowledge graph on one publicly available and one proprietary dataset. |
Zero-Shot Rationalization by Multi-Task Transfer Learning from Question Answering (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to extract rationales from input text are difficult and impractical. |
| Approach: | They propose a method that leverages multi-task learning and transfer learning to generate rationales through question answering in a zero-shot fashion. |
| Outcome: | The proposed method achieves comparable or even better performance without supervised signal for two benchmark rationalization datasets. |
Improving Zero-Shot Cross-lingual Transfer for Multilingual Question Answering over Knowledge Graph (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches to solve multilingual question answering over knowledge graph (KGQA) use of bilingual lexicon induction to map training questions into those in target language circumvents language inconsistency . |
| Approach: | They propose to use bilingual lexicon induction to map training questions in source and target languages as augmented training data to minimize syntax-disorder. |
| Outcome: | The proposed model narrows the gap in zero-shot cross-lingual transfer between source and target languages. |
Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering (2022.coling-1)
Copied to clipboard
| Challenge: | Generative question answering (QA) models generate answers to complex questions, but their mechanism for doing so is still poorly understood. |
| Approach: | They decompose multi-hop questions into multiple corresponding single-hop question chains and find marked inconsistency in QA models’ answers on these pairs of ostensibly identical question chains. |
| Outcome: | The proposed models lack zero-shot multi-hop reasoning ability when trained on single-hop questions and on logical forms. |
ZEBRA: Zero-Shot Example-Based Retrieval Augmentation for Commonsense Question Answering (2024.emnlp-main)
Copied to clipboard
| Challenge: | Current Large Language Models (LLMs) have shown strong reasoning capabilities in commonsense question answering benchmarks, but the process underlying their success remains largely opaque. |
| Approach: | They propose a zero-shot question answering framework that combines retrieval, case-based reasoning and introspection to improve the model's performance and interpretability. |
| Outcome: | The proposed framework outperforms existing LLMs and previous knowledge integration approaches in commonsense reasoning benchmarks and achieves an average accuracy improvement of 4.5 points. |
Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning (2022.naacl-main)
Copied to clipboard
| Challenge: | Currently, commonsense reasoning systems are limited by expensive data annotations and overfitting to a specific benchmark. |
| Approach: | They propose to transform a commonsense knowledge graph into synthetic QA-form samples for model training. |
| Outcome: | The proposed framework improves performance with multiple commonsense KGs on five commonsensense reasoning benchmarks. |
Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks (N18-2)
Copied to clipboard
| Challenge: | Existing work on simple question answering over knowledge graphs involves increasingly complex NN architectures. |
| Approach: | They propose to decompose the problem into entity detection, entity linking, relation prediction, evidence combination and heuristics. |
| Outcome: | The proposed approach outperforms existing models and benchmarks on a simple QA task. |
MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering (2024.acl-long)
Copied to clipboard
| Challenge: | Recent advances in few-shot question answering rely on pre-trained large language models and fine-tuning in specific settings. |
| Approach: | They propose to select the most informative data for fine-tuning to improve efficiency . they use an approximate graph algorithm and unsupervised question generation to generate QA pairs . |
| Outcome: | The proposed framework improves the performance of the few-shot question answering task on the open-domain QA task. |