This paper describes the submission to SemEval-2023 Task 2: Multilingual Complex Named Entity Recognition (MultiCoNER II) by team SAB. This task aims to encourage growth in the field of Named Entity Recognition (NER) by focusing on complex and difficult categories of entities, in 12 different language tracks. The task of NER has historically shown the best results when a model incorporates an external knowledge base or gazetteer, however, less research has been applied to examining the effects of incorporating linguistic information into the model. In this task, we explored combining NER, part-of-speech (POS), and dependency relation labels into a multi-task model and report on the findings. We determine that the addition of POS and dependency relation information in this manner does not improve results.

This paper introduces the model and settings submitted to the INLG 2022 DialogSum Chal- lenge, a shared task to generate summaries of real-life scenario dialogues between two peo- ple. In this paper, we explored using interme- diate task transfer learning, reported speech, and the use of a supplementary dataset in addi- tion to our base fine-tuned BART model. How- ever, we did not use such a method in our final model, as none improved our results. Our final model for this dialogue task achieved scores only slightly below the top submission, with hidden test set scores of 49.62, 24.98, 46.25 and 91.54 for ROUGE-1, ROUGE-2, ROUGE-L and BERTSCORE respectively. The top submitted models will also receive human evaluation.