Recognizing Semantic Relations by Combining Transformers and Fully Connected Models (2020.lrec-1)
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| Challenge: | Current approaches to recognizing semantic relations between words are limited and require a word-path model. |
| Approach: | They propose a distributional approach that is based on an attention-based transformer and a word path model that combines useful properties of a convolutional network with a fully connected language model. |
| Outcome: | The proposed model outperforms the state-of-the-art in terms of performance and data sources. |
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| Challenge: | Existing approaches to recognize lexical semantic relations between word pairs require that word pairs co-occur in a sentence. |
| Approach: | They propose to exploit lexico-syntactic paths between two target words to exploit the semantic relations between word pairs. |
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Within-Between Lexical Relation Classification (2020.emnlp-main)
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| Challenge: | Existing methods for recognizing lexical-semantic relations between words are path-based and distributional. |
| Approach: | They propose a novel Within-Between Relation model for recognizing lexical-semantic relations between words. |
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LXMERT: Learning Cross-Modality Encoder Representations from Transformers (D19-1)
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| Challenge: | Existing models with better representations of visual content and language have been developed for visual-content understanding. |
| Approach: | They propose a framework to learn vision-and-language connections from Transformers models . they pre-train a large-scale Transformer model with large amounts of image-and sentence pairs . |
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Encoding and Fusing Semantic Connection and Linguistic Evidence for Implicit Discourse Relation Recognition (2022.findings-acl)
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| Challenge: | Existing studies use one attention mechanism to improve contextual semantic representation learning for implicit discourse relation recognition (IDRR). |
| Approach: | They propose a Multi-Attentive Neural Fusion model to fuse linguistic evidence and semantic connection for IDRR by using a Dual Attention Network and an Offset Matrix Network. |
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Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling (D19-1)
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| Challenge: | Semantic similarity modeling is central to many NLP problems such as question answering. |
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Matching the Blanks: Distributional Similarity for Relation Learning (P19-1)
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| Challenge: | Efforts to build general purpose relation extractors that can model arbitrary relations are limited in their ability to generalize. |
| Approach: | They propose to build task-agnostic relation representations solely from entity-linked text to extend Harris’ distributional hypothesis to relations. |
| Outcome: | The proposed representations outperform previous methods on SemEval 2010 Task 8, KBP37, and TACRED even without using any of the task’s training data. |
Language Models and Semantic Relations: A Dual Relationship (2024.lrec-main)
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| Challenge: | Existing studies on language models for the extraction of semantic relations have focused on injecting semantic knowledge into these models to enhance them. |
| Approach: | They propose to extract lexical semantic relations from a BERT model and inject them into it using unsupervised methods based on semantic similarity at word and sentence levels. |
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Modeling Graph Structure in Transformer for Better AMR-to-Text Generation (D19-1)
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| Challenge: | Recent studies on AMR-to-text generation formalize the task as a sequence-tosequence learning problem . previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs. |
| Approach: | They propose a structure-aware self-attention approach to model the relations between indirectly connected concepts in the seq2seq model. |
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Semantically Driven Sentence Fusion: Modeling and Evaluation (2020.findings-emnlp)
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| Challenge: | Sentence fusion is the task of joining related sentences into coherent text. |
| Approach: | They propose a method where ground-truth solutions are automatically expanded into multiple references via curated equivalence classes of connective phrases. |
| Outcome: | The proposed approach improves on state-of-the-art models by expanding ground-truth solutions into multiple references. |
Learning Relatedness between Types with Prototypes for Relation Extraction (2021.eacl-main)
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| Challenge: | Existing datasets have no intrinsic Ontology for relation types. |
| Approach: | They propose to use prototypical examples to represent each relation type and use them to augment related types from a different dataset. |
| Outcome: | The proposed model improves on a baseline with multi-task learning between datasets to obtain better representation for relations. |