Search Results for author:
Found 54 papers, 33 papers with code
A light way to collect comparable corpora from the Web
In this work we aim to reduce the amount of time and resources spent for tasks 1 and 2.
Lexical Query Modeling in Session Search
Lexical query modeling has been the leading paradigm for session search.
Learning Latent Vector Spaces for Product Search
We introduce a novel latent vector space model that jointly learns the latent representations of words, e-commerce products and a mapping between the two without the need for explicit annotations.
Pyndri: a Python Interface to the Indri Search Engine
We introduce pyndri, a Python interface to the Indri search engine.
Semantic Entity Retrieval Toolkit
Unsupervised learning of low-dimensional, semantic representations of words and entities has recently gained attention.
Structural Regularities in Text-based Entity Vector Spaces
We discover how clusterings of experts correspond to committees in organizations, the ability of expert representations to encode the co-author graph, and the degree to which they encode academic rank.
Neural Vector Spaces for Unsupervised Information Retrieval
We propose the Neural Vector Space Model (NVSM), a method that learns representations of documents in an unsupervised manner for news article retrieval.
Examining Cooperation in Visual Dialog Models
In this work we propose a blackbox intervention method for visual dialog models, with the aim of assessing the contribution of individual linguistic or visual components.
Semi-supervised Ensemble Learning with Weak Supervision for Biomedical Relationship Extraction
To tackle this problem, we propose a methodology for extending training datasets to arbitrarily big sizes and training complex, data-hungry models using weak supervision.
Improved and Robust Controversy Detection in General Web Pages Using Semantic Approaches under Large Scale Conditions
This paper investigates neural classifiers as a more robust methodology for controversy detection in general web pages.
Query Generation for Patent Retrieval with Keyword Extraction based on Syntactic Features
The performance is measured on the search results of a query consisting of the extracted keywords.
Global Aggregations of Local Explanations for Black Box models
We present Global Aggregations of Local Explanations (GALE) with the objective to provide insights in a model's global decision making process.
A distantly supervised dataset for automated data extraction from diagnostic studies
Systematic reviews are important in evidence based medicine, but are expensive to produce.
Learning to Ask: Question-based Sequential Bayesian Product Search
The method learns the product relevance as well as the reward of the potential questions to be asked to the user by being trained on the search history and purchase behavior of a specific user together with that of other users.
Conversations with Search Engines: SERP-based Conversational Response Generation
In this paper, we address the problem of answering complex information needs by conversing conversations with search engines, in the sense that users can express their queries in natural language, and directly receivethe information they need from a short system response in a conversational manner.
WN-Salience: A Corpus of News Articles with Entity Salience Annotations
To support research on entity salience, we present a new dataset, the WikiNews Salience dataset (WN-Salience), which can be used to benchmark tasks such as entity salience detection and salient entity linking.
Query Resolution for Conversational Search with Limited Supervision
Context from the conversational history can be used to arrive at a better expression of the current turn query, defined as the task of query resolution.
An Analysis of Mixed Initiative and Collaboration in Information-Seeking Dialogues
The ability to engage in mixed-initiative interaction is one of the core requirements for a conversational search system.
Knowledge Graph Simple Question Answering for Unseen Domains
Knowledge graph simple question answering (KGSQA), in its standard form, does not take into account that human-curated question answering training data only cover a small subset of the relations that exist in a Knowledge Graph (KG), or even worse, that new domains covering unseen and rather different to existing domains relations are added to the KG.
Towards Question-based Recommender Systems
Previous conversational recommender systems ask users to express their preferences over items or item facets.
An Empirical Study of Clarifying Question-Based Systems
Search and recommender systems that take the initiative to ask clarifying questions to better understand users' information needs are receiving increasing attention from the research community.
Analysing the Effect of Clarifying Questions on Document Ranking in Conversational Search
Recent research on conversational search highlights the importance of mixed-initiative in conversations.
A Benchmark for Lease Contract Review
We focus on supporting the review of lease agreements, a contract type that has received little attention in the legal information extraction literature, and we define the types of entities and red flags needed for that task.
Ranking Clarifying Questions Based on Predicted User Engagement
This research demonstrates the potential for ranking clarification panes based on lexical information only and can serve as a first neural baseline for future research to improve on.
Category Aware Explainable Conversational Recommendation
We first perform an explainable user model in the form of preferences over the items' categories, and then use the category preferences to recommend items.
A Large-Scale Analysis of Mixed Initiative in Information-Seeking Dialogues for Conversational Search
These datasets were collected to inform different dialogue-based tasks including conversational search.
Combining Lexical and Dense Retrieval for Computationally Efficient Multi-hop Question Answering
In simple open-domain question answering (QA), dense retrieval has become one of the standard approaches for retrieving the relevant passages to infer an answer.
Multi-hop Question Answering
Open-Domain Question Answering
+2
Few-Shot Electronic Health Record Coding through Graph Contrastive Learning
We seek to improve the performance for both frequent and rare ICD codes by using a contrastive graph-based EHR coding framework, CoGraph, which re-casts EHR coding as a few-shot learning task.
VerbCL: A Dataset of Verbatim Quotes for Highlight Extraction in Case Law
Citing legal opinions is a key part of legal argumentation, an expert task that requires retrieval, extraction and summarization of information from court decisions.
Legal Search in Case Law and Statute Law
In this work we describe a method to identify document pairwise relevance in the context of a typical legal document collection: limited resources, long queries and long documents.
Robustness Evaluation of Entity Disambiguation Using Prior Probes:the Case of Entity Overshadowing
We evaluate and report the performance of popular EL systems on the ShadowLink benchmark.
Analysing Mixed Initiatives and Search Strategies during Conversational Search
In this paper, we present a model for conversational search -- from which we instantiate different observed conversational search strategies, where the agent elicits: (i) Feedback-First, or (ii) Feedback-After.
Cross-Market Product Recommendation
We introduce and formalize the problem of cross-market product recommendation, i. e., market adaptation.
Paint4Poem: A Dataset for Artistic Visualization of Classical Chinese Poems
We create abenchmark for Paint4Poem: we train two representative text-to-image generation models: AttnGAN and MirrorGAN, and evaluate theirperformance regarding painting pictorial quality, painting stylistic relevance, and semantic relevance between poems and paintings. The results indicate that the models are able to generate paintings that have good pictorial quality and mimic Feng Zikai's style, but thereflection of poem semantics is limited.
Controllable Recommenders using Deep Generative Models and Disentanglement
We show that by updating the disentangled latent space based on user feedback, and by exploiting the generative nature of the recommender, controlled and personalized recommendations can be produced.
Tackling Query-Focused Summarization as A Knowledge-Intensive Task: A Pilot Study
In this dataset, answering the query requires document retrieval from a knowledge corpus.
Towards Building Economic Models of Conversational Search
Various conceptual and descriptive models of conversational search have been proposed in the literature -- while useful, they do not provide insights into how interaction between the agent and user would change in response to the costs and benefits of the different interactions.
Parameter-Efficient Abstractive Question Answering over Tables or Text
In this work, we study parameter-efficient abstractive QA in encoder-decoder models over structured tabular data and unstructured textual data using only 1. 5% additional parameters for each modality.
Zero-shot Query Contextualization for Conversational Search
Current conversational passage retrieval systems cast conversational search into ad-hoc search by using an intermediate query resolution step that places the user's question in context of the conversation.
Analysing the Robustness of Dual Encoders for Dense Retrieval Against Misspellings
Dense retrieval is becoming one of the standard approaches for document and passage ranking.
On the Impact of Speech Recognition Errors in Passage Retrieval for Spoken Question Answering
Finally, we create a new dataset with questions voiced by human users and use their transcriptions to show that the retrieval performance can further degrade when dealing with natural ASR noise instead of synthetic ASR noise.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
Focusing on Context is NICE: Improving Overshadowed Entity Disambiguation
Entity disambiguation (ED) is the task of mapping an ambiguous entity mention to the corresponding entry in a structured knowledge base.
Natural Language Processing of Aviation Occurrence Reports for Safety Management
In support of safety data analysis, reports are often categorized according to a taxonomy.
Market-Aware Models for Efficient Cross-Market Recommendation
We consider the cross-market recommendation (CMR) task, which involves recommendation in a low-resource target market using data from a richer, auxiliary source market.
Perspectives on Large Language Models for Relevance Judgment
When asked, large language models (LLMs) like ChatGPT claim that they can assist with relevance judgments but it is not clear whether automated judgments can reliably be used in evaluations of retrieval systems.
Generating Synthetic Documents for Cross-Encoder Re-Rankers: A Comparative Study of ChatGPT and Human Experts
We introduce a new dataset, ChatGPT-RetrievalQA, and compare the effectiveness of models fine-tuned on LLM-generated and human-generated data.
MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering
Recent advances in tabular question answering (QA) with large language models are constrained in their coverage and only answer questions over a single table.
Stress Testing BERT Anaphora Resolution Models for Reaction Extraction in Chemical Patents
The high volume of published chemical patents and the importance of a timely acquisition of their information gives rise to automating information extraction from chemical patents.
Data Augmentation for Conversational AI
This tutorial provides a comprehensive and up-to-date overview of DA approaches in the context of conversational systems.
Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactions
Our framework involves two LLMs interacting on a specific topic, with the first LLM acting as a student, generating questions to explore a given search topic.
Self-seeding and Multi-intent Self-instructing LLMs for Generating Intent-aware Information-Seeking dialogs
Furthermore, we propose SOLID-RL, which is further trained to generate a dialog in one step on the data generated by SOLID.
Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge
The two prominent approaches to enhance the performance of LLMs on low-frequent topics are: Retrieval Augmented Generation (RAG) and fine-tuning (FT) over synthetic data.
Improving the Robustness of Dense Retrievers Against Typos via Multi-Positive Contrastive Learning
Dense retrieval has become the new paradigm in passage retrieval.
Robustness Evaluation of Entity Disambiguation Using Prior Probes: the Case of Entity Overshadowing
We evaluate and report the performance of popular EL systems on the ShadowLink benchmark.
