In information retrieval (IR), domain adaptation is the process of adapting a retrieval model to a new domain whose data distribution is different from the source domain.
To address this issue, one can train NRMs via weak supervision, where a large dataset is automatically generated using an existing ranking model (called the weak labeler) for training NRMs.
In this work, we analyze a range of non-stochastic fairness-aware ranking and diversity metrics to evaluate the extent to which argument stances are fairly exposed in argument retrieval systems.
To quickly identify user intent and reduce effort during interactions, we propose an intent clarification task based on yes/no questions where the system needs to ask the correct question about intents within the fewest conversation turns.
We first conduct sparse retrieval with BM25 and study expanding the question with object names and image captions.
Our method is more flexible as it can handle both span answers and freeform answers.
Here we focus on context-aware models to leverage the rich contextual information available to mobile devices.
Asking clarifying questions in response to ambiguous or faceted queries has been recognized as a useful technique for various information retrieval systems, especially conversational search systems with limited bandwidth interfaces.
We build an end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader that are all based on Transformers.
Aware of these limitations, we propose a transformer-based embedding model (TEM) for personalized product search, which could dynamically control the influence of personalization by encoding the sequence of query and user's purchase history with a transformer architecture.
RTM conducts review-level matching between the user and item, where each review has a dynamic effect according to the context in the sequence.
Redundancy-aware extractive summarization systems score the redundancy of the sentences to be included in a summary either jointly with their salience information or separately as an additional sentence scoring step.
We also perform case studies and analysis of learned user intent and its impact on response ranking in information-seeking conversations to provide interpretation of results.
When performing cross-language information retrieval (CLIR) for lower-resourced languages, a common approach is to retrieve over the output of machine translation (MT).
Specifically, we propose to model the "search and purchase" behavior as a dynamic relation between users and items, and create a dynamic knowledge graph based on both the multi-relational product data and the context of the search session.
In this paper, we study RF techniques based on both long-term and short-term context dependencies in multi-page product search.
So in this paper, we propose a conversational paradigm for product search driven by non-relevant items, based on which fine-grained feedback is collected and utilized to show better results in the next iteration.
In this paper, we formulate the problem of personalized product search and conduct large-scale experiments with search logs sampled from a commercial e-commerce search engine.
First, we propose a positional history answer embedding method to encode conversation history with position information using BERT in a natural way.
In this paper, we formulate the task of asking clarifying questions in open-domain information-seeking conversational systems.
Despite the importance of the task, the community still feels the significant lack of large-scale non-factoid question answering collections with real questions and comprehensive relevance judgments.
One of the major challenges to multi-turn conversational search is to model the conversation history to answer the current question.
The bidirectional encoder representations from transformers (BERT) model has recently advanced the state-of-the-art in passage re-ranking.
In this paper, we propose a hybrid neural conversation model that combines the merits of both response retrieval and generation methods.
Ranking models lie at the heart of research on information retrieval (IR).
Specifically, the data selector "acts" on the source domain data to find a subset for optimization of the TL model, and the performance of the TL model can provide "rewards" in turn to update the selector.
We conduct extensive experiments to analyze and compare IRF with the standard top-k RF framework on document and passage retrieval.
In this work, we propose a standalone neural ranking model (SNRM) by introducing a sparsity property to learn a latent sparse representation for each query and document.
Ranked #12 on Ad-Hoc Information Retrieval on TREC Robust04
Building multi-turn information-seeking conversation systems is an important and challenging research topic.
We use an adversarial discriminator and train our neural ranking model on a small set of domains.
Our models and research findings provide new insights on how to utilize external knowledge with deep neural models for response selection and have implications for the design of the next generation of information-seeking conversation systems.
Understanding and characterizing how people interact in information-seeking conversations is crucial in developing conversational search systems.
Specifically, we employ a recurrent neural network to sequentially encode the top results using their feature vectors, learn a local context model and use it to re-rank the top results.
We find that the problem of estimating a propensity model from click data is a dual problem of unbiased learning to rank.
As an alternative to question answering methods based on feature engineering, deep learning approaches such as convolutional neural networks (CNNs) and Long Short-Term Memory Models (LSTMs) have recently been proposed for semantic matching of questions and answers.
Ranked #12 on Question Answering on TrecQA
Specifically, our model employs a joint deep architecture at the query term level for relevance matching.
Ranked #14 on Ad-Hoc Information Retrieval on TREC Robust04
In this framework, each type of information source (review text, product image, numerical rating, etc) is adopted to learn the corresponding user and item representations based on available (deep) representation learning architectures.
We further evaluate the neural matching models in the next question prediction task in conversations.
This is the motivation for developing unsupervised relevance-based word embedding models that learn word representations based on query-document relevance information.
Our experiments indicate that employing proper objective functions and letting the networks to learn the input representation based on weakly supervised data leads to impressive performance, with over 13% and 35% MAP improvements over the BM25 model on the Robust and the ClueWeb collections.
Ranked #8 on Ad-Hoc Information Retrieval on TREC Robust04 (MAP metric)
Recent work in Information Retrieval (IR) using Deep Learning models has yielded state of the art results on a variety of IR tasks.