Knowledge and pre-trained language models inside and out: a deep-dive into datasets and external knowledge

Pre-trained Language Models (PLMs) have greatly advanced the performance of various NLP tasks and have undoubtedly been serving as foundation models for this field. These pre-trained models are able to capture rich semantic patterns from large-scale text corpora and learn high-quality representations of texts. However, such models still have shortcomings - they underperform when faced with tasks that requires implicit external knowledge to be understood, which is difficult to learn with commonly employed pre-training objectives. Moreover, there lacks a comprehensive understanding of PLMs’ behaviour in learning knowledge during the fine-tuning phase. Therefore, in order to address the aforementioned challenges, we propose a set of approaches to inject external knowledge into PLMs and demonstrate experiments investigating their behaviour in learning knowledge during the fine-tuning phase, primarily focusing on Sentiment Analysis, Question Answering and Video Question Answering. Specifically, we introduce novel approaches explicitly using textual historical reviews of users and products for improving sentiment analysis. To overcome the problem of context-question lexical overlap and data scarcity for question generation, we propose a novel method making use of linguistic and semantic knowledge with heuristics. Additionally, we explore how to utilise multimodal (visual and acoustic) information/knowledge to improve Video Question Answering. Experiments conducted on benchmark datasets show that our proposed approaches achieve superior performance compared to state-of-the-art models, demonstrating the effectiveness of our methods for injecting external knowledge. Furthermore, we conduct a set of experiments investigating the learning of knowledge for PLMs for question answering under various scenarios. Results reveal that the internal characteristics of QA datasets can pose strong bias for PLMs when learning from downstream tasks datasets. Finally, we present an in-depth discussion of future directions for improving PLMs with external knowledge

Discourse-Level Language Understanding with Deep Learning

Designing computational models that can understand language at a human level is a foundational goal in the field of natural language processing (NLP). Given a sentence, machines are capable of translating it into many different languages, generating a corresponding syntactic parse tree, marking words that refer to people or places, and much more. These tasks are solved by statistical machine learning algorithms, which leverage patterns in large datasets to build predictive models. Many recent advances in NLP are due to deep learning models (parameterized as neural networks), which bypass user-specified features in favor of building representations of language directly from the text. Despite many deep learning-fueled advances at the word and sentence level, however, computers still struggle to understand high-level discourse structure in language, or the way in which authors combine and order different units of text (e.g., sentences, paragraphs, chapters) to express a coherent message or narrative. Part of the reason is data-related, as there are no existing datasets for many contextual language-based problems, and some tasks are too complex to be framed as supervised learning problems; for the latter type, we must either resort to unsupervised learning or devise training objectives that simulate the supervised setting. Another reason is architectural: neural networks designed for sentence-level tasks require additional functionality, interpretability, and efficiency to operate at the discourse level. In this thesis, I design deep learning architectures for three NLP tasks that require integrating information across high-level linguistic context: question answering, fictional relationship understanding, and comic book narrative modeling. While these tasks are very different from each other on the surface, I show that similar neural network modules can be used in each case to form contextual representations

Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges