Neural Natural Language Inference Models Enhanced with External Knowledge (P18-1)
Copied to clipboard
| Challenge: | Existing datasets that allow for complex models to be trained are limited . if data is not available, can machines learn all knowledge needed to perform natural language inference? |
| Approach: | They propose to enrich neural natural language inference models with external knowledge . they propose to use this knowledge to build NLI models to leverage it . |
| Outcome: | The proposed models improve on the SNLI and MultiNLI datasets. |
Similar Papers
Deep Learning for Natural Language Inference (N19-5)
Copied to clipboard
| Challenge: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning for language understanding and reasoning. |
| Approach: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development and cutting- edge deep learning models. |
| Outcome: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning model for language understanding and reasoning. |
Improving Unsupervised Commonsense Reasoning Using Knowledge-Enabled Natural Language Inference (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Recent methods based on pre-trained language models have shown strong supervised performance on commonsense reasoning. |
| Approach: | They propose to use a common framework to solve commonsense reasoning tasks using a dataset from NLI. |
| Outcome: | The proposed method achieves state-of-the-art unsupervised performance on two commonsense reasoning tasks. |
Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference (D18-1)
Copied to clipboard
| Challenge: | In this paper, we examine the behavior of deep learning models in their intermediate layers . saliency determines what is critical for the final decision of a deep model . |
| Approach: | They propose to interpret the intermediate layers of deep models by visualizing the saliency of attention and LSTM gating signals. |
| Outcome: | The proposed methods reveal interesting insights and identify critical information contributing to the model decisions. |
Enhancing Neural Data-To-Text Generation Models with External Background Knowledge (D19-1)
Copied to clipboard
| Challenge: | Recent neural models for data-to-text generation rely on parallel pairs of data and text to learn writing knowledge. |
| Approach: | They propose to enhance neural models with external knowledge to improve fidelity of generated text. |
| Outcome: | The proposed model improves on Wikipedia infobox-to-text datasets on 21 datasets. |
Knowledge-Enhanced Natural Language Inference Based on Knowledge Graphs (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to natural language inference rely on semantic knowledge, but background knowledge is limited to a few specific types. |
| Approach: | They propose a Knowledge Graph-enhanced NLI model that leverages background knowledge stored in knowledge graphs to facilitate inference. |
| Outcome: | The proposed model can leverage background knowledge stored in knowledge graphs to perform the task. |
A synthetic data approach for domain generalization of NLI models (2024.acl-long)
Copied to clipboard
| Challenge: | Natural Language Inference (NLI) datasets are important benchmark tasks for LLMs . however, their realistic performance on out-of-distribution/domain data is less well-understood . a T5-small model trained with our data improves around 7% on average compared to the best alternative dataset . |
| Approach: | They propose a new approach for generating NLI data in diverse domains and lengths . they show that models trained on this data have the best generalization to completely new downstream test settings . |
| Outcome: | The proposed model can be trained on datasets with high-quality examples with meaningful premises and high accuracy. |
Simple but Challenging: Natural Language Inference Models Fail on Simple Sentences (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Natural language inference (NLI) tasks are difficult to perform on large datasets . a small number of simple sentences can improve model performance, authors say . |
| Approach: | They propose to use syntactically simple sentences to test the inference ability of NLI models. |
| Outcome: | The proposed set of simple sentences shows that the models fine-tuned on MNLI and SNLI perform poorly on Simple Pair. |
OCNLI: Original Chinese Natural Language Inference (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent efforts to extend natural language understanding to other languages have focused on (automatically) translating existing English datasets. |
| Approach: | They propose to use a Chinese dataset to generate annotated sentences from native speakers specializing in linguistics to elicit annotations. |
| Outcome: | The proposed dataset does not rely on automatic translation or non-expert annotation. instead, it elicits annotations from native speakers specializing in linguistics. |
Dual Inference for Improving Language Understanding and Generation (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have exploited the duality of the task pairs in machine translation and speech recognition. |
| Approach: | They propose to leverage the duality in the inference stage without retraining whole models. |
| Outcome: | The proposed method is effective in both NLU and NLG tasks, providing the great potential of practical use. |
Atomic Inference for NLI with Generated Facts as Atoms (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing models that can provide accurate explanations are not interpretable, i.e. they do not reflect the inner workings of the model. |
| Approach: | They propose to use LLM-generated facts as atoms to make interpretable models that can be used to make accurate predictions for each component part of an input. |
| Outcome: | The proposed method outperforms existing methods on natural language understanding tasks with a multi-stage fact generation process and a training regime that incorporates the facts. |