Information Access Using Neural Networks For Diverse Domains And Sources

The ever-increasing volume of web-based documents poses a challenge in efficiently accessing specialized knowledge from domain-specific sources, requiring a profound understanding of the domain and substantial comprehension effort. Although natural language technologies, such as information retrieval and machine reading compression systems, offer rapid and accurate information retrieval, their performance in specific domains is hindered by training on general domain datasets. Creating domain-specific training datasets, while effective, is time-consuming, expensive, and heavily reliant on domain experts. This thesis presents a comprehensive exploration of efficient technologies to address the challenge of information access in specific domains, focusing on retrieval-based systems encompassing question answering and ranking. We begin with a comprehensive introduction to the information access system. We demonstrated the structure of a information access system through a typical open-domain question-answering task. We outline its two major components: retrieval and reader models, and the design choice for each part. We focus on mainly three points: 1) the design choice of the connection of the two components. 2) the trade-off associated with the retrieval model and the best frontier in practice. 3) a data augmentation method to adapt the reader model, trained initially on closed-domain datasets, to effectively answer questions in the retrieval-based setting. Subsequently, we discuss various methods enabling system adaptation to specific domains. Transfer learning techniques are presented, including generation as data augmentation, further pre-training, and progressive domain-clustered training. We also present a novel zero-shot re-ranking method inspired by the compression-based distance. We summarize the conclusions and findings gathered from the experiments. Moreover, the exploration extends to retrieval-based systems beyond textual corpora. We explored the search system for an e-commerce database, wherein natural language queries are combined with user preference data to facilitate the retrieval of relevant products. To address the challenges, including noisy labels and cold start problems, for the retrieval-based e-commerce ranking system, we enhanced model training through cascaded training and adversarial sample weighting. Another scenario we investigated is the search system in the math domain, characterized by the unique role of formulas and distinct features compared to textual searches. We tackle the math related search problem by combining neural ranking models with structual optimized algorithms. Finally, we summarize the research findings and future research directions

Less is More: Restricted Representations for Better Interpretability and Generalizability

Deep neural networks are prevalent in supervised learning for large amounts of tasks such as image classification, machine translation and even scientific discovery. Their success is often at the sacrifice of interpretability and generalizability. The increasing complexity of models and involvement of the pre-training process make the inexplicability more imminent. The outstanding performance when labeled data are abundant while prone to overfit when labeled data are limited demonstrates the difficulty of deep neural networks' generalizability to different datasets. This thesis aims to improve interpretability and generalizability by restricting representations. We choose to approach interpretability by focusing on attribution analysis to understand which features contribute to prediction on BERT, and to approach generalizability by focusing on effective methods in a low-data regime. We consider two strategies of restricting representations: (1) adding bottleneck, and (2) introducing compression. Given input x, suppose we want to learn y with the latent representation z (i.e. x→z→y), adding bottleneck means adding function R such that L(R(z)) < L(z) and introducing compression means adding function R so that L(R(y)) < L(y) where L refers to the number of bits. In other words, the restriction is added either in the middle of the pipeline or at the end of it. We first introduce how adding information bottleneck can help attribution analysis and apply it to investigate BERT's behavior on text classification in Chapter 3. We then extend this attribution method to analyze passage reranking in Chapter 4, where we conduct a detailed analysis to understand cross-layer and cross-passage behavior. Adding bottleneck can not only provide insight to understand deep neural networks but can also be used to increase generalizability. In Chapter 5, we demonstrate the equivalence between adding bottleneck and doing neural compression. We then leverage this finding with a framework called Non-Parametric learning by Compression with Latent Variables (NPC-LV), and show how optimizing neural compressors can be used in the non-parametric image classification with few labeled data. To further investigate how compression alone helps non-parametric learning without latent variables (NPC), we carry out experiments with a universal compressor gzip on text classification in Chapter 6. In Chapter 7, we elucidate methods of adopting the perspective of doing compression but without the actual process of compression using T5. Using experimental results in passage reranking, we show that our method is highly effective in a low-data regime when only one thousand query-passage pairs are available. In addition to the weakly supervised scenario, we also extend our method to large language models like GPT under almost no supervision --- in one-shot and zero-shot settings. The experiments show that without extra parameters or in-context learning, GPT can be used for semantic similarity, text classification, and text ranking and outperform strong baselines, which is presented in Chapter 8. The thesis proposes to tackle two big challenges in machine learning --- "interpretability" and "generalizability" through restricting representation. We provide both theoretical derivation and empirical results to show the effectiveness of using information-theoretic approaches. We not only design new algorithms but also provide numerous insights on why and how "compression" is so important in understanding deep neural networks and improving generalizability

Conditional Kolmogorov complexity and universal probability

The conditional in conditional Kolmogorov complexity commonly is taken to be a finite binary string. The Coding Theorem of L.A. Levin connects unconditional prefix Kolmogorov complexity with the discrete universal distribution. The least upper semicomputable code-length is up to a constant equal to the negative logarithm of the greatest lower semicomputable probability mass function. We investigate conditional versions of the Coding Theorem for singleton and joint probability distributions under alternative definitions. No conditional Coding Theorem holds in the singleton case, in the joint case under the customary definition of conditional probability, but it does hold in the joint case under an alternative definition