Towards Human-centered Proactive Conversational Agents

Recent research on proactive conversational agents (PCAs) mainly focuses on improving the system's capabilities in anticipating and planning action sequences to accomplish tasks and achieve goals before users articulate their requests. This perspectives paper highlights the importance of moving towards building human-centered PCAs that emphasize human needs and expectations, and that considers ethical and social implications of these agents, rather than solely focusing on technological capabilities. The distinction between a proactive and a reactive system lies in the proactive system's initiative-taking nature. Without thoughtful design, proactive systems risk being perceived as intrusive by human users. We address the issue by establishing a new taxonomy concerning three key dimensions of human-centered PCAs, namely Intelligence, Adaptivity, and Civility. We discuss potential research opportunities and challenges based on this new taxonomy upon the five stages of PCA system construction. This perspectives paper lays a foundation for the emerging area of conversational information retrieval research and paves the way towards advancing human-centered proactive conversational systems.

SEINE: SEgment-based Indexing for NEural information retrieval

Many early neural Information Retrieval (NeurIR) methods are re-rankers that rely on a traditional first-stage retriever due to expensive query time computations. Recently, representation-based retrievers have gained much attention, which learns query representation and document representation separately, making it possible to pre-compute document representations offline and reduce the workload at query time. Both dense and sparse representation-based retrievers have been explored. However, these methods focus on finding the representation that best represents a text (aka metric learning) and the actual retrieval function that is responsible for similarity matching between query and document is kept at a minimum by using dot product. One drawback is that unlike traditional term-level inverted index, the index formed by these embeddings cannot be easily re-used by another retrieval method. Another drawback is that keeping the interaction at minimum hurts retrieval effectiveness. On the contrary, interaction-based retrievers are known for their better retrieval effectiveness. In this paper, we propose a novel SEgment-based Neural Indexing method, SEINE, which provides a general indexing framework that can flexibly support a variety of interaction-based neural retrieval methods. We emphasize on a careful decomposition of common components in existing neural retrieval methods and propose to use segment-level inverted index to store the atomic query-document interaction values. Experiments on LETOR MQ2007 and MQ2008 datasets show that our indexing method can accelerate multiple neural retrieval methods up to 28-times faster without sacrificing much effectiveness.

Sequencing Matters: A Generate-Retrieve-Generate Model for Building Conversational Agents

This paper contains what the Georgetown InfoSense group has done in regard to solving the challenges presented by TREC iKAT 2023. Our submitted runs outperform the median runs by a significant margin, exhibiting superior performance in nDCG across various cut numbers and in overall success rate. Our approach uses a Generate-Retrieve-Generate method, which we've found to greatly outpace Retrieve-Then-Generate approaches for the purposes of iKAT. Our solution involves the use of Large Language Models (LLMs) for initial answers, answer grounding by BM25, passage quality filtering by logistic regression, and answer generation by LLMs again. We leverage several purpose-built Language Models, including BERT, Chat-based, and text-to-transfer-based models, for text understanding, classification, generation, and summarization. The official results of the TREC evaluation contradict our initial self-evaluation, which may suggest that a decrease in the reliance on our retrieval and classification methods is better. Nonetheless, our findings suggest that the sequence of involving these different components matters, where we see an essentiality of using LLMs before using search engines.

GazBy: Gaze-Based BERT Model to Incorporate Human Attention in Neural Information Retrieval

This paper is interested in investigating whether human gaze signals can be leveraged to improve state-of-the-art search engine performance and how to incorporate this new input signal marked by human attention into existing neural retrieval models. In this paper, we propose GazBy ({\bf Gaz}e-based {\bf B}ert model for document relevanc{\bf y}), a light-weight joint model that integrates human gaze fixation estimation into transformer models to predict document relevance, incorporating more nuanced information about cognitive processing into information retrieval (IR). We evaluate our model on the Text Retrieval Conference (TREC) Deep Learning (DL) 2019 and 2020 Tracks. Our experiments show encouraging results and illustrate the effective and ineffective entry points for using human gaze to help with transformer-based neural retrievers. With the rise of virtual reality (VR) and augmented reality (AR), human gaze data will become more available. We hope this work serves as a first step exploring using gaze signals in modern neural search engines.

Incorporating Voice Instructions in Model-Based Reinforcement Learning for Self-Driving Cars

This paper presents a novel approach that supports natural language voice instructions to guide deep reinforcement learning (DRL) algorithms when training self-driving cars. DRL methods are popular approaches for autonomous vehicle (AV) agents. However, most existing methods are sample- and time-inefficient and lack a natural communication channel with the human expert. In this paper, how new human drivers learn from human coaches motivates us to study new ways of human-in-the-loop learning and a more natural and approachable training interface for the agents. We propose incorporating natural language voice instructions (NLI) in model-based deep reinforcement learning to train self-driving cars. We evaluate the proposed method together with a few state-of-the-art DRL methods in the CARLA simulator. The results show that NLI can help ease the training process and significantly boost the agents' learning speed.

Feature Modulation to Improve Struggle Detection in Web Search: A Psychological Approach

Searcher struggle is important feedback to Web search engines. Existing Web search struggle detection methods rely on effort-based features to identify the struggling moments. Their underlying assumption is that the more effort a user spends, the more struggling the user may be. However, recent studies have suggested this simple association might be incorrect. This paper proposes a new feature modulation method for struggle detection and refers to the reversal theory in psychology. The reversal theory (RT) points out that instead of having a static personality trait, people constantly switch between opposite psychological states, complicating the relationship between the efforts they spend and the level of frustration they feel. Supported by the theory, our method modulates the effort-based features based on RT's bi-modal arousal model. Evaluations on week-long Web search logs confirm that the proposed method can statistically significantly improve state-of-the-art struggle detection methods.

High-Quality Diversification for Task-Oriented Dialogue Systems

Many task-oriented dialogue systems use deep reinforcement learning (DRL) to learn policies that respond to the user appropriately and complete the tasks successfully. Training DRL agents with diverse dialogue trajectories prepare them well for rare user requests and unseen situations. One effective diversification method is to let the agent interact with a diverse set of learned user models. However, trajectories created by these artificial user models may contain generation errors, which can quickly propagate into the agent's policy. It is thus important to control the quality of the diversification and resist the noise. In this paper, we propose a novel dialogue diversification method for task-oriented dialogue systems trained in simulators. Our method, Intermittent Short Extension Ensemble (I-SEE), constrains the intensity to interact with an ensemble of diverse user models and effectively controls the quality of the diversification. Evaluations on the Multiwoz dataset show that I-SEE successfully boosts the performance of several state-of-the-art DRL dialogue agents.

Balancing Reinforcement Learning Training Experiences in Interactive Information Retrieval

Interactive Information Retrieval (IIR) and Reinforcement Learning (RL) share many commonalities, including an agent who learns while interacts, a long-term and complex goal, and an algorithm that explores and adapts. To successfully apply RL methods to IIR, one challenge is to obtain sufficient relevance labels to train the RL agents, which are infamously known as sample inefficient. However, in a text corpus annotated for a given query, it is not the relevant documents but the irrelevant documents that predominate. This would cause very unbalanced training experiences for the agent and prevent it from learning any policy that is effective. Our paper addresses this issue by using domain randomization to synthesize more relevant documents for the training. Our experimental results on the Text REtrieval Conference (TREC) Dynamic Domain (DD) 2017 Track show that the proposed method is able to boost an RL agent's learning effectiveness by 22\% in dealing with unseen situations.

Information Retrieval and Its Sister Disciplines

This article presents a summary graph to show the relationships between Information Retrieval (IR) and other related disciplines. The figure tells the key differences between them and the conditions under which one would transition into another.

Corpus-Level End-to-End Exploration for Interactive Systems

A core interest in building Artificial Intelligence (AI) agents is to let them interact with and assist humans. One example is Dynamic Search (DS), which models the process that a human works with a search engine agent to accomplish a complex and goal-oriented task. Early DS agents using Reinforcement Learning (RL) have only achieved limited success for (1) their lack of direct control over which documents to return and (2) the difficulty to recover from wrong search trajectories. In this paper, we present a novel corpus-level end-to-end exploration (CE3) method to address these issues. In our method, an entire text corpus is compressed into a global low-dimensional representation, which enables the agent to gain access to the full state and action spaces, including the under-explored areas. We also propose a new form of retrieval function, whose linear approximation allows end-to-end manipulation of documents. Experiments on the Text REtrieval Conference (TREC) Dynamic Domain (DD) Track show that CE3 outperforms the state-of-the-art DS systems.