Search Results for author: Andrew Yates

Found 48 papers, 25 papers with code

Bag-of-Words Baselines for Semantic Code Search

no code implementations ACL (NLP4Prog) 2021 Xinyu Zhang, Ji Xin, Andrew Yates, Jimmy Lin

The task of semantic code search is to retrieve code snippets from a source code corpus based on an information need expressed in natural language.

Code Search Information Retrieval +2

PRIDE: Predicting Relationships in Conversations

no code implementations EMNLP 2021 Anna Tigunova, Paramita Mirza, Andrew Yates, Gerhard Weikum

Automatically extracting interpersonal relationships of conversation interlocutors can enrich personal knowledge bases to enhance personalized search, recommenders and chatbots.

A Little Bit Is Worse Than None: Ranking with Limited Training Data

no code implementations EMNLP (sustainlp) 2020 Xinyu Zhang, Andrew Yates, Jimmy Lin

Researchers have proposed simple yet effective techniques for the retrieval problem based on using BERT as a relevance classifier to rerank initial candidates from keyword search.

Passage Retrieval Retrieval

CHARM: Inferring Personal Attributes from Conversations

no code implementations EMNLP 2020 Anna Tigunova, Andrew Yates, Paramita Mirza, Gerhard Weikum

Personal knowledge about users{'} professions, hobbies, favorite food, and travel preferences, among others, is a valuable asset for individualized AI, such as recommenders or chatbots.

Attribute Keyword Extraction +2

Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation?

no code implementations2 May 2024 Ming Li, Yuanna Liu, Sami Jullien, Mozhdeh Ariannezhad, Mohammad Aliannejadi, Andrew Yates, Maarten de Rijke

So far, most NBR studies have focused on optimizing the accuracy of the recommendation, whereas optimizing for beyond-accuracy metrics, e. g., item fairness and diversity remains largely unexplored.

Fairness Navigate +2

Meta-Task Prompting Elicits Embedding from Large Language Models

no code implementations28 Feb 2024 Yibin Lei, Di wu, Tianyi Zhou, Tao Shen, Yu Cao, Chongyang Tao, Andrew Yates

In this work, we introduce a new unsupervised embedding method, Meta-Task Prompting with Explicit One-Word Limitation (MetaEOL), for generating high-quality sentence embeddings from Large Language Models (LLMs) without the need for model fine-tuning or task-specific engineering.

Semantic Textual Similarity Sentence +2

Corpus-Steered Query Expansion with Large Language Models

1 code implementation28 Feb 2024 Yibin Lei, Yu Cao, Tianyi Zhou, Tao Shen, Andrew Yates

Recent studies demonstrate that query expansions generated by large language models (LLMs) can considerably enhance information retrieval systems by generating hypothetical documents that answer the queries as expansions.

Information Retrieval Retrieval

Multimodal Learned Sparse Retrieval with Probabilistic Expansion Control

1 code implementation27 Feb 2024 Thong Nguyen, Mariya Hendriksen, Andrew Yates, Maarten de Rijke

Our proposed approach efficiently transforms dense vectors from a frozen dense model into sparse lexical vectors.

Image Retrieval Retrieval +1

Demonstrating and Reducing Shortcuts in Vision-Language Representation Learning

1 code implementation27 Feb 2024 Maurits Bleeker, Mariya Hendriksen, Andrew Yates, Maarten de Rijke

Hence, contrastive losses are not sufficient to learn task-optimal representations, i. e., representations that contain all task-relevant information shared between the image and associated captions.

Contrastive Learning Representation Learning

Multimodal Learned Sparse Retrieval for Image Suggestion

no code implementations12 Feb 2024 Thong Nguyen, Mariya Hendriksen, Andrew Yates

Motivated by this, in this work, we explore the application of LSR in the multi-modal domain, i. e., we focus on Multi-Modal Learned Sparse Retrieval (MLSR).

Image Captioning Retrieval +1

Replicating Relevance-Ranked Synonym Discovery in a New Language and Domain

no code implementations2 Oct 2023 Andrew Yates, Michael Unterkalmsteiner

We replicate prior work on ranking domain-specific synonyms in the consumer health domain by applying the approach to a new language and domain: identifying Swedish language synonyms in the building construction domain.

Learning-To-Rank

Masked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery Shopping

1 code implementation2 Aug 2023 Ming Li, Mozhdeh Ariannezhad, Andrew Yates, Maarten de Rijke

In next basket recommendation (NBR), it is useful to distinguish between repeat items, i. e., items that a user has consumed before, and explore items, i. e., items that a user has not consumed before.

Next-basket recommendation

Generative Retrieval as Dense Retrieval

no code implementations20 Jun 2023 Thong Nguyen, Andrew Yates

Generative retrieval is a promising new neural retrieval paradigm that aims to optimize the retrieval pipeline by performing both indexing and retrieval with a single transformer model.

Retrieval

Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training

1 code implementation5 Jun 2023 Yibin Lei, Liang Ding, Yu Cao, Changtong Zan, Andrew Yates, DaCheng Tao

Dense retrievers have achieved impressive performance, but their demand for abundant training data limits their application scenarios.

Contrastive Learning Retrieval

Adapting Learned Sparse Retrieval for Long Documents

1 code implementation29 May 2023 Thong Nguyen, Sean MacAvaney, Andrew Yates

We investigate existing aggregation approaches for adapting LSR to longer documents and find that proximal scoring is crucial for LSR to handle long documents.

Language Modelling Masked Language Modeling +1

MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering

1 code implementation22 May 2023 Vaishali Pal, Andrew Yates, Evangelos Kanoulas, Maarten de Rijke

Recent advances in tabular question answering (QA) with large language models are constrained in their coverage and only answer questions over a single table.

Question Answering

A Unified Framework for Learned Sparse Retrieval

1 code implementation23 Mar 2023 Thong Nguyen, Sean MacAvaney, Andrew Yates

We then reproduce all prominent methods using a common codebase and re-train them in the same environment, which allows us to quantify how components of the framework affect effectiveness and efficiency.

Retrieval

Reducing Predictive Feature Suppression in Resource-Constrained Contrastive Image-Caption Retrieval

1 code implementation28 Apr 2022 Maurits Bleeker, Andrew Yates, Maarten de Rijke

We add an additional decoder to the contrastive ICR framework, to reconstruct the input caption in a latent space of a general-purpose sentence encoder, which prevents the image and caption encoder from suppressing predictive features.

Contrastive Learning Retrieval +1

Zero-shot Query Contextualization for Conversational Search

1 code implementation22 Apr 2022 Antonios Minas Krasakis, Andrew Yates, Evangelos Kanoulas

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.

Conversational Search Passage Retrieval +1

Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires

1 code implementation ACL 2022 Thong Nguyen, Andrew Yates, Ayah Zirikly, Bart Desmet, Arman Cohan

In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9's symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach.

Depression Detection Domain Generalization

Language Models As or For Knowledge Bases

no code implementations10 Oct 2021 Simon Razniewski, Andrew Yates, Nora Kassner, Gerhard Weikum

Pre-trained language models (LMs) have recently gained attention for their potential as an alternative to (or proxy for) explicit knowledge bases (KBs).

Position

Personalized Entity Search by Sparse and Scrutable User Profiles

no code implementations10 Sep 2021 Ghazaleh Haratinezhad Torbati, Andrew Yates, Gerhard Weikum

Prior work on personalizing web search results has focused on considering query-and-click logs to capture users individual interests.

Re-Ranking

You Get What You Chat: Using Conversations to Personalize Search-based Recommendations

no code implementations10 Sep 2021 Ghazaleh Haratinezhad Torbati, Andrew Yates, Gerhard Weikum

The paper develops an expressive model and effective methods for personalizing search-based entity recommendations.

Re-Ranking

How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset

1 code implementation17 May 2021 Iain Mackie, Jeffery Dalton, Andrew Yates

Deep Learning Hard (DL-HARD) is a new annotated dataset designed to more effectively evaluate neural ranking models on complex topics.

CEQE: Contextualized Embeddings for Query Expansion

no code implementations9 Mar 2021 Shahrzad Naseri, Jeffrey Dalton, Andrew Yates, James Allan

We find that CEQE outperforms static embedding-based expansion methods on multiple collections (by up to 18% on Robust and 31% on Deep Learning on average precision) and also improves over proven probabilistic pseudo-relevance feedback (PRF) models.

Re-Ranking Retrieval

Pretrained Transformers for Text Ranking: BERT and Beyond

1 code implementation NAACL 2021 Jimmy Lin, Rodrigo Nogueira, Andrew Yates

There are two themes that pervade our survey: techniques for handling long documents, beyond typical sentence-by-sentence processing in NLP, and techniques for addressing the tradeoff between effectiveness (i. e., result quality) and efficiency (e. g., query latency, model and index size).

Information Retrieval Retrieval +1

PARADE: Passage Representation Aggregation for Document Reranking

1 code implementation20 Aug 2020 Canjia Li, Andrew Yates, Sean MacAvaney, Ben He, Yingfei Sun

In this work, we explore strategies for aggregating relevance signals from a document's passages into a final ranking score.

Document Ranking Knowledge Distillation

RedDust: a Large Reusable Dataset of Reddit User Traits

no code implementations LREC 2020 Anna Tigunova, Paramita Mirza, Andrew Yates, Gerhard Weikum

To the best of our knowledge, RedDust is the first annotated language resource about Reddit users at large scale.

Attribute

Listening between the Lines: Learning Personal Attributes from Conversations

1 code implementation24 Apr 2019 Anna Tigunova, Andrew Yates, Paramita Mirza, Gerhard Weikum

Open-domain dialogue agents must be able to converse about many topics while incorporating knowledge about the user into the conversation.

Attribute

Using Multi-Sense Vector Embeddings for Reverse Dictionaries

1 code implementation WS 2019 Michael A. Hedderich, Andrew Yates, Dietrich Klakow, Gerard de Melo

However, they typically cannot serve as a drop-in replacement for conventional single-sense embeddings, because the correct sense vector needs to be selected for each word.

NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval

1 code implementation EMNLP 2018 Canjia Li, Yingfei Sun, Ben He, Le Wang, Kai Hui, Andrew Yates, Le Sun, Jungang Xu

Pseudo-relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new query terms, thereby reducing the effect of query-document vocabulary mismatches.

Ad-Hoc Information Retrieval Information Retrieval +1

Depression and Self-Harm Risk Assessment in Online Forums

no code implementations EMNLP 2017 Andrew Yates, Arman Cohan, Nazli Goharian

We propose methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrate that our approach outperforms strong previously proposed methods for identifying such posts.

Content-Based Weak Supervision for Ad-Hoc Re-Ranking

1 code implementation1 Jul 2017 Sean MacAvaney, Andrew Yates, Kai Hui, Ophir Frieder

One challenge with neural ranking is the need for a large amount of manually-labeled relevance judgments for training.

Information Retrieval Re-Ranking

Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval

3 code implementations30 Jun 2017 Kai Hui, Andrew Yates, Klaus Berberich, Gerard de Melo

Neural IR models, such as DRMM and PACRR, have achieved strong results by successfully capturing relevance matching signals.

Ad-Hoc Information Retrieval Retrieval

DE-PACRR: Exploring Layers Inside the PACRR Model

no code implementations27 Jun 2017 Andrew Yates, Kai Hui

Recent neural IR models have demonstrated deep learning's utility in ad-hoc information retrieval.

Ad-Hoc Information Retrieval Information Retrieval +1

PACRR: A Position-Aware Neural IR Model for Relevance Matching

3 code implementations EMNLP 2017 Kai Hui, Andrew Yates, Klaus Berberich, Gerard de Melo

In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query.

Ad-Hoc Information Retrieval Information Retrieval +2

Triaging Content Severity in Online Mental Health Forums

no code implementations22 Feb 2017 Arman Cohan, Sydney Young, Andrew Yates, Nazli Goharian

Our analysis on the interaction of the moderators with the users further indicates that without an automatic way to identify critical content, it is indeed challenging for the moderators to provide timely response to the users in need.

Effects of Sampling on Twitter Trend Detection

no code implementations LREC 2016 Andrew Yates, Alek Kolcz, Nazli Goharian, Ophir Frieder

In this work we use a larger feed to investigate the effects of sampling on Twitter trend detection.

A Framework for Public Health Surveillance

no code implementations LREC 2014 Andrew Yates, Jon Parker, Nazli Goharian, Ophir Frieder

With the rapid growth of social media, there is increasing potential to augment traditional public health surveillance methods with data from social media.

Information Retrieval

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