Search Results for author:
Found 47 papers, 25 papers with code
PRIDE: Predicting Relationships in Conversations
Automatically extracting interpersonal relationships of conversation interlocutors can enrich personal knowledge bases to enhance personalized search, recommenders and chatbots.
Bag-of-Words Baselines for Semantic Code Search
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.
CHARM: Inferring Personal Attributes from Conversations
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.
A Little Bit Is Worse Than None: Ranking with Limited Training Data
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.
Corpus-Steered Query Expansion with Large Language Models
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.
Meta-Task Prompting Elicits Embedding from Large Language Models
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.
Multimodal Learned Sparse Retrieval with Probabilistic Expansion Control
Our proposed approach efficiently transforms dense vectors from a frozen dense model into sparse lexical vectors.
Demonstrating and Reducing Shortcuts in Vision-Language Representation Learning
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.
Multimodal Learned Sparse Retrieval for Image Suggestion
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).
Recommendations by Concise User Profiles from Review Text
Recommender systems are most successful for popular items and users with ample interactions (likes, ratings etc.).
Replicating Relevance-Ranked Synonym Discovery in a New Language and Domain
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.
Masked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery Shopping
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.
Generative Retrieval as Dense Retrieval
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.
Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training
Dense retrievers have achieved impressive performance, but their demand for abundant training data limits their application scenarios.
Adapting Learned Sparse Retrieval for Long Documents
We investigate existing aggregation approaches for adapting LSR to longer documents and find that proximal scoring is crucial for LSR to handle long documents.
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.
A Unified Framework for Learned Sparse Retrieval
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.
Reducing Predictive Feature Suppression in Resource-Constrained Contrastive Image-Caption Retrieval
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.
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.
Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires
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.
Language Models As or For Knowledge Bases
Pre-trained language models (LMs) have recently gained attention for their potential as an alternative to (or proxy for) explicit knowledge bases (KBs).
Personalized Entity Search by Sparse and Scrutable User Profiles
Prior work on personalizing web search results has focused on considering query-and-click logs to capture users individual interests.
You Get What You Chat: Using Conversations to Personalize Search-based Recommendations
The paper develops an expressive model and effective methods for personalizing search-based entity recommendations.
How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset
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
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.
Simplified Data Wrangling with ir_datasets
Managing the data for Information Retrieval (IR) experiments can be challenging.
Pretrained Transformers for Text Ranking: BERT and Beyond
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).
BERT-QE: Contextualized Query Expansion for Document Re-ranking
Query expansion aims to mitigate the mismatch between the language used in a query and in a document.
PARADE: Passage Representation Aggregation for Document Reranking
In this work, we explore strategies for aggregating relevance signals from a document's passages into a final ranking score.
Ranked #2 on
Ad-Hoc Information Retrieval
on TREC Robust04
RedDust: a Large Reusable Dataset of Reddit User Traits
To the best of our knowledge, RedDust is the first annotated language resource about Reddit users at large scale.
STANCY: Stance Classification Based on Consistency Cues
Controversial claims are abundant in online media and discussion forums.
Listening between the Lines: Learning Personal Attributes from Conversations
Open-domain dialogue agents must be able to converse about many topics while incorporating knowledge about the user into the conversation.
CEDR: Contextualized Embeddings for Document Ranking
We call this joint approach CEDR (Contextualized Embeddings for Document Ranking).
Ranked #3 on
Ad-Hoc Information Retrieval
on TREC Robust04
Investigating Retrieval Method Selection with Axiomatic Features
We consider algorithm selection in the context of ad-hoc information retrieval.
Using Multi-Sense Vector Embeddings for Reverse Dictionaries
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
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.
Ranked #9 on
Ad-Hoc Information Retrieval
on TREC Robust04
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning
Misinformation such as fake news is one of the big challenges of our society.
RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses
Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media.
SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions
Mental health is a significant and growing public health concern.
Depression and Self-Harm Risk Assessment in Online Forums
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
One challenge with neural ranking is the need for a large amount of manually-labeled relevance judgments for training.
Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval
Neural IR models, such as DRMM and PACRR, have achieved strong results by successfully capturing relevance matching signals.
DE-PACRR: Exploring Layers Inside the PACRR Model
Recent neural IR models have demonstrated deep learning's utility in ad-hoc information retrieval.
PACRR: A Position-Aware Neural IR Model for Relevance Matching
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.
Triaging Content Severity in Online Mental Health Forums
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
In this work we use a larger feed to investigate the effects of sampling on Twitter trend detection.
A Framework for Public Health Surveillance
With the rapid growth of social media, there is increasing potential to augment traditional public health surveillance methods with data from social media.
