Jeff Da at COIN - Shared Task: BIG MOOD: Relating Transformers to Explicit Commonsense Knowledge (D19-60)
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| Challenge: | Recent studies show that large-scale pre-training models can be effective for large datasets. |
| Approach: | They propose a method of integrating contextual embeddings with commonsense graph embeddINGs by preprocessing knowledge bases and aligning tokens between misaligned tokenization methods. |
| Outcome: | The proposed method achieves higher accuracy than BERT and scores highest without pretraining. |
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Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations (D19-60)
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| Challenge: | Pretrained deep contextual representations have advanced the state-of-the-art on various commonsense NLP tasks, but we lack a concrete understanding of their capabilities. |
| Approach: | They investigate BERT's ability to encode various commonsense features in its embedding space, but are still deficient in many areas. |
| Outcome: | The proposed model improves performance on a downstream commonsense reasoning task while using minimal data. |
Coconut: Contextualized Commonsense Unified Transformers for Graph-Based Commonsense Augmentation of Language Models (2024.findings-acl)
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| Challenge: | Existing studies show that pre-trained language models lack commonsense knowledge . |
| Approach: | They propose a contextualized knowledge prompting scheme to guide the contextualization of structured commonsense knowledge based on large language models. |
| Outcome: | The proposed approach outperforms the state-of-the-art technique by an average of 5.8%. |
Pretrained Knowledge Base Embeddings for improved Sentential Relation Extraction (2022.acl-srw)
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| Challenge: | Existing models that perform explicit on-task training of graph embeddings are inadequate. |
| Approach: | They propose to combine pretrained knowledge base graph embeddings with transformer based language models to improve performance on sentential Relation Extraction task. |
| Outcome: | The proposed model outperforms state-of-the-art models on the sentential Relation Extraction task. |
Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing (D19-1)
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| Challenge: | Existing approaches to learn cross-lingual word embeddings in a contextual space are lacking. |
| Approach: | They propose a method to generate cross-lingual contextualized word embeddings using pre-trained BERT models by learning a linear transformation from contextual word alignments. |
| Outcome: | The proposed approach outperforms state-of-the-art models on zero-shot cross-lingual transfer parsing and is highly competitive with existing models. |
CoLAKE: Contextualized Language and Knowledge Embedding (2020.coling-main)
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| Challenge: | Existing models for integrating factual knowledge into pre-trained language models are shallow, static, and separately pre-train entities. |
| Approach: | They propose a method which integrates knowledge contexts from large-scale knowledge bases into a unified data structure. |
| Outcome: | The proposed model outperforms existing models on knowledge-driven tasks and knowledge probing tasks. |
Identify, Align, and Integrate: Matching Knowledge Graphs to Commonsense Reasoning Tasks (2021.eacl-main)
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| Challenge: | Empirically, we investigate KG matches for the SocialIQA, Physical IQA, and MCScript2.0 datasets with 3 diverse KGs: ATOMIC (SIQA), ConceptNet (Speer et al., 2017), and an automatically constructed instructional KG based on WikiHow (Ostermann e., 2019b). |
| Approach: | They propose a method to assess how well a candidate KG can fill in knowledge gaps for a given task by using commonsense probes. |
| Outcome: | Empirically, we show that the proposed KG-to-task match is a good match for socialIQA, physical IQA, and MCScript2.0 datasets with 3 diverse KGs: ATOMIC, ConceptNet, and an instructional KG based on WikiHow. |
KARNA at COIN Shared Task 1: Bidirectional Encoder Representations from Transformers with relational knowledge for machine comprehension with common sense (D19-60)
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| Challenge: | Using Bidirectional Encoder Representations from Transformers(BERT) and external relational knowledge from ConceptNet, we are able to achieve an accuracy of 73.3 % on the official test data. |
| Approach: | They propose a model that uses Bidirectional Encoder Representations from Transformers and ConceptNet to tackle the problem of commonsense inference in natural language processing. |
| Outcome: | The proposed model achieves 73.3 % accuracy on the official test data. |
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT (2020.findings-emnlp)
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| Challenge: | Existing methods to enhance BERT with factual knowledge about entities require no additional pretraining and no changes to the encoder itself. |
| Approach: | They propose a way to inject factual knowledge into the pretrained BERT model by aligning Wikipedia2Vec entity vectors with BERT's native wordpiece vector space and feeding the aligned entity vector into BERT as if they were wordpieces. |
| Outcome: | The proposed version outperforms baseline models on unsupervised question answering, supervised relation classification and entity linking tasks. |
HittER: Hierarchical Transformers for Knowledge Graph Embeddings (2021.emnlp-main)
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| Challenge: | Existing knowledge graph embedding methods to learn representations of knowledge graphs are conceptually simple and can be applied to tasks like factoid question answering (Saxena et al., 2020) and reasoning. |
| Approach: | They propose a Hierarchical Transformer model to jointly learn Entity-relation composition and Relational contextualization based on a source entity’s neighborhood. |
| Outcome: | The proposed model achieves state-of-the-art on multiple link prediction datasets and can be integrated into BERT and demonstrate its effectiveness on two Freebase factoid question answering datasets. |
Do Syntax Trees Help Pre-trained Transformers Extract Information? (2021.eacl-main)
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| Challenge: | Recent work suggests that incorporating syntax information from dependency trees can improve task-specific transformer models. |
| Approach: | They propose to incorporate dependency tree information into pre-trained transformers for three tasks . they propose a late fusion approach and a joint fusion technique to infuses syntax structure into attention layers. |
| Outcome: | The proposed models obtain state-of-the-art results on SRL and relation extraction tasks. |