Papers by Mihael Arcan
Analysing the Correlation between Lexical Ambiguity and Translation Quality in a Multimodal Setting using WordNet (2022.naacl-srw)
Copied to clipboard
| Challenge: | Recent studies in machine translation have been focusing on using visual information to improve the translation quality of sentences. |
| Approach: | They propose to use visual information to improve the output quality of a text-based translation model by extracting ambiguity scores from WordNet. |
| Outcome: | The proposed model improves translation quality for all sentences in the English-German dataset. |
Suggest me a movie for tonight: Leveraging Knowledge Graphs for Conversational Recommendation (2020.coling-main)
Copied to clipboard
| Challenge: | Recent studies show that knowledge graphs are incomplete since they do not contain all factual information present on the web. |
| Approach: | They propose to use knowledge graphs to improve the performance of conversational recommender systems by incorporating pre-trained embeddings from subgraphs and positional embeddments into their models. |
| Outcome: | The proposed method improves by 5.62% over the state-of-the-art method on multiple metrics on the recommendation task. |
Automatic Enrichment of Terminological Resources: the IATE RDF Example (L18-1)
Copied to clipboard
| Challenge: | a recent paper aims to automate the maintenance of terminological resources. |
| Approach: | They propose automatic approaches to maintain and increase lexical coverage of knowledge bases by using machine translation and multilingual word sense disambiguation. |
| Outcome: | The proposed approach outperforms the existing methods with random sentences in most languages . |
CALM-Bench: A Multi-task Benchmark for Evaluating Causality-Aware Language Models (2023.findings-eacl)
Copied to clipboard
| Challenge: | Recent advances in foundation language models have shown the efficacy of pre-trained models across diverse QA tasks. |
| Approach: | They propose a multi-task benchmark for evaluating causality-aware language models to unify causal QA research. |
| Outcome: | The proposed model outperforms single-task fine-tuned models on the CALM-Bench tasks. |