125 research outputs found
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users
Static recommendation methods like collaborative filtering suffer from the
inherent limitation of performing real-time personalization for cold-start
users. Online recommendation, e.g., multi-armed bandit approach, addresses this
limitation by interactively exploring user preference online and pursuing the
exploration-exploitation (EE) trade-off. However, existing bandit-based methods
model recommendation actions homogeneously. Specifically, they only consider
the items as the arms, being incapable of handling the item attributes, which
naturally provide interpretable information of user's current demands and can
effectively filter out undesired items. In this work, we consider the
conversational recommendation for cold-start users, where a system can both ask
the attributes from and recommend items to a user interactively. This important
scenario was studied in a recent work. However, it employs a hand-crafted
function to decide when to ask attributes or make recommendations. Such
separate modeling of attributes and items makes the effectiveness of the system
highly rely on the choice of the hand-crafted function, thus introducing
fragility to the system. To address this limitation, we seamlessly unify
attributes and items in the same arm space and achieve their EE trade-offs
automatically using the framework of Thompson Sampling. Our Conversational
Thompson Sampling (ConTS) model holistically solves all questions in
conversational recommendation by choosing the arm with the maximal reward to
play. Extensive experiments on three benchmark datasets show that ConTS
outperforms the state-of-the-art methods Conversational UCB (ConUCB) and
Estimation-Action-Reflection model in both metrics of success rate and average
number of conversation turns.Comment: TOIS 202
Reconciliation of Pre-trained Models and Prototypical Neural Networks in Few-shot Named Entity Recognition
Incorporating large-scale pre-trained models with the prototypical neural
networks is a de-facto paradigm in few-shot named entity recognition. Existing
methods, unfortunately, are not aware of the fact that embeddings from
pre-trained models contain a prominently large amount of information regarding
word frequencies, biasing prototypical neural networks against learning word
entities. This discrepancy constrains the two models' synergy. Thus, we propose
a one-line-code normalization method to reconcile such a mismatch with
empirical and theoretical grounds. Our experiments based on nine benchmark
datasets show the superiority of our method over the counterpart models and are
comparable to the state-of-the-art methods. In addition to the model
enhancement, our work also provides an analytical viewpoint for addressing the
general problems in few-shot name entity recognition or other tasks that rely
on pre-trained models or prototypical neural networks.Comment: Findings of EMNLP 202
Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration
Conversational systems based on Large Language Models (LLMs), such as
ChatGPT, show exceptional proficiency in context understanding and response
generation. However, despite their impressive capabilities, they still possess
limitations, such as providing randomly-guessed answers to ambiguous queries or
failing to refuse users' requests, both of which are considered aspects of a
conversational agent's proactivity. This raises the question of whether
LLM-based conversational systems are equipped to handle proactive dialogue
problems. In this work, we conduct a comprehensive analysis of LLM-based
conversational systems, specifically focusing on three aspects of proactive
dialogue systems: clarification, target-guided, and non-collaborative
dialogues. To trigger the proactivity of LLMs, we propose the Proactive
Chain-of-Thought prompting scheme, which augments LLMs with the goal planning
capability over descriptive reasoning chains. Empirical findings are discussed
to promote future studies on LLM-based proactive dialogue systems.Comment: Work in progres
Ultra-Short Wave Communication Squelch Algorithm Based on Deep Neural Network
The squelch problem of ultra-short wave communication under non-stationary noise and low Signal-to-Noise Ratio (SNR) in a complex electromagnetic environment is still challenging. To alleviate the problem, we proposed a squelch algorithm for ultra-short wave communication based on a deep neural network and the traditional energy decision method. The proposed algorithm first predicts the speech existence probability using a three-layer Gated Recurrent Unit (GRU) with the speech banding spectrum as the feature. Then it gets the final squelch result by combining the strength of the signal energy and the speech existence probability. Multiple simulations and experiments are done to verify the robustness and effectiveness of the proposed algorithm. We simulate the algorithm in three situations: the typical Amplitude Modulation (AM) and Frequency Modulation (FM) in the ultra-short wave communication under different SNR environments, the non-stationary burst-like noise environments, and the real received signal of the ultra-short wave radio. The experimental results show that the proposed algorithm performs better than the traditional squelch methods in all the simulations and experiments. In particular, the false alarm rate of the proposed squelch algorithm for non-stationary burst-like noise is significantly lower than that of traditional squelch methods
Generalized Relation Learning with Semantic Correlation Awareness for Link Prediction
Developing link prediction models to automatically complete knowledge graphs
has recently been the focus of significant research interest. The current
methods for the link prediction taskhavetwonaturalproblems:1)the relation
distributions in KGs are usually unbalanced, and 2) there are many unseen
relations that occur in practical situations. These two problems limit the
training effectiveness and practical applications of the existing link
prediction models. We advocate a holistic understanding of KGs and we propose
in this work a unified Generalized Relation Learning framework GRL to address
the above two problems, which can be plugged into existing link prediction
models. GRL conducts a generalized relation learning, which is aware of
semantic correlations between relations that serve as a bridge to connect
semantically similar relations. After training with GRL, the closeness of
semantically similar relations in vector space and the discrimination of
dissimilar relations are improved. We perform comprehensive experiments on six
benchmarks to demonstrate the superior capability of GRL in the link prediction
task. In particular, GRL is found to enhance the existing link prediction
models making them insensitive to unbalanced relation distributions and capable
of learning unseen relations.Comment: Preprint of accepted AAAI2021 pape
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