Hamed Zamani
16 papers in the PaperMetrix corpus
Papers by this author
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Neural Query Performance Prediction using Weak Supervision from Multiple Signals
2018
Predicting the performance of a search engine for a given query is a fundamental and challenging task in information retrieval. Accurate performance predictors can be used in various ways, such as triggering an action, choosing …
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Macaw: An Extensible Conversational Information Seeking Platform
2020
Conversational information seeking (CIS) has been recognized as a major emerging research area in information retrieval. Such research will require data and tools, to allow the implementation and study of conversational systems. This paper introduces …
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Learning a Joint Search and Recommendation Model from User-Item Interactions
2020
Existing learning to rank models for information retrieval are trained based on explicit or implicit query-document relevance information. In this paper, we study the task of learning a retrieval model based on user-item interactions. Our …
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MIMICS: A Large-Scale Data Collection for Search Clarification
2020 · arXiv (Cornell University)
Search clarification has recently attracted much attention due to its applications in search engines. It has also been recognized as a major component in conversational information seeking systems. Despite its importance, the research community still …
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Towards Multi-Modal Conversational Information Seeking
2021
Recent research on conversational information seeking (CIS) mostly focuses on uni-modal interactions and information items. This per- spective paper highlights the importance of moving towards de- veloping and evaluating multi-modal conversational information seeking (MMCIS) systems …
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LaMP: When Large Language Models Meet Personalization
2023 · arXiv (Cornell University)
This paper highlights the importance of personalization in large language models and introduces the LaMP benchmark -- a novel benchmark for training and evaluating language models for producing personalized outputs. LaMP offers a comprehensive evaluation …
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Editable User Profiles for Controllable Text Recommendations
2023
Methods for making high-quality recommendations often rely on learning latent representations from interaction data. These methods, while performant, do not provide ready mechanisms for users to control the recommendation they receive. Our work tackles this …
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Simulating Task-Oriented Dialogues with State Transition Graphs and Large Language Models
2024 · arXiv (Cornell University)
This paper explores SynTOD, a new synthetic data generation approach for developing end-to-end Task-Oriented Dialogue (TOD) Systems capable of handling complex tasks such as intent classification, slot filling, conversational question-answering, and retrieval-augmented response generation, without …
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LongLaMP: A Benchmark for Personalized Long-form Text Generation
2024 · arXiv (Cornell University)
Long-text generation is seemingly ubiquitous in real-world applications of large language models such as generating an email or writing a review. Despite the fundamental importance and prevalence of long-text generation in many practical applications, existing …
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Embedding-based Query Language Models
2016
Word embeddings, which are low-dimensional vector representations of vocabulary terms that capture the semantic similarity between them, have recently been shown to achieve impressive performance in many natural language processing tasks. The use of word …
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Neural Ranking Models with Weak Supervision
2017
Despite the impressive improvements achieved by unsupervised deep neural networks in computer vision and NLP tasks, such improvements have not yet been observed in ranking for information retrieval. The reason may be the complexity of …
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Relevance-based Word Embedding
2017
Learning a high-dimensional dense representation for vocabulary terms, also known as a word embedding, has recently attracted much attention in natural language processing and information retrieval tasks. The embedding vectors are typically learned based on …
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Current challenges and visions in music recommender systems research
2018 · International Journal of Multimedia Information Retrieval
Music recommender systems (MRSs) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user’s fingertip. …
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Neural Ranking Models with Multiple Document Fields
2018
Deep neural networks have recently shown promise in the ad-hoc retrieval task. However, such models have often been based on one field of the document, for example considering document title only or document body only. …
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From Neural Re-Ranking to Neural Ranking
2018
The availability of massive data and computing power allowing for effective data driven neural approaches is having a major impact on machine learning and information retrieval research, but these models have a basic problem with …
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Asking Clarifying Questions in Open-Domain Information-Seeking Conversations
2019
Users often fail to formulate their complex information needs in a single query. As a consequence, they may need to scan multiple result pages or reformulate their queries, which may be a frustrating experience. Alternatively, …