Search Results for author:
Found 190 papers, 120 papers with code
Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted Trees
Stochastic learning to rank (LTR) is a recent branch in the LTR field that concerns the optimization of probabilistic ranking models.
Context Does Matter: Implications for Crowdsourced Evaluation Labels in Task-Oriented Dialogue Systems
Using the first user utterance as context leads to consistent ratings, akin to those obtained using the entire dialogue, with significantly reduced annotation effort.
Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset
However, these gains in click prediction do not translate to enhanced ranking performance on expert relevance annotations, implying that conclusions strongly depend on how success is measured in this benchmark.
Multi-granular Adversarial Attacks against Black-box Neural Ranking Models
However, limiting perturbations to a single level of granularity may reduce the flexibility of adversarial examples, thereby diminishing the potential threat of the attack.
Query Performance Prediction using Relevance Judgments Generated by Large Language Models
This allows us to predict any IR evaluation measure using the generated relevance judgments as pseudo-labels; Also, this allows us to interpret predicted IR evaluation measures, and identify, track and rectify errors in generated relevance judgments to improve QPP quality.
Generative Retrieval as Multi-Vector Dense Retrieval
Both methods compute relevance as a sum of products of query and document vectors and an alignment matrix.
Are Large Language Models Good at Utility Judgments?
Retrieval-augmented generation (RAG) is considered to be a promising approach to alleviate the hallucination issue of large language models (LLMs), and it has received widespread attention from researchers recently.
CAUSE: Counterfactual Assessment of User Satisfaction Estimation in Task-Oriented Dialogue Systems
In this work, we leverage large language models (LLMs) and unlock their ability to generate satisfaction-aware counterfactual dialogues to augment the set of original dialogues of a test collection.
RankingSHAP -- Listwise Feature Attribution Explanations for Ranking Models
We evaluate RankingSHAP for commonly used learning-to-rank datasets to showcase the more nuanced use of an attribution method while highlighting the limitations of selection-based explanations.
Listwise Generative Retrieval Models via a Sequential Learning Process
Specifically, we view the generation of a ranked docid list as a sequence learning process: at each step we learn a subset of parameters that maximizes the corresponding generation likelihood of the $i$-th docid given the (preceding) top $i-1$ docids.
Measuring Bias in a Ranked List using Term-based Representations
With TExFAIR, we extend the AWRF framework to allow for the evaluation of fairness in settings with term-based representations of groups in documents in a ranked list.
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 with Probabilistic Expansion Control
Our proposed approach efficiently transforms dense vectors from a frozen dense model into sparse lexical vectors.
Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning
Parameter-efficient fine-tuning (PEFT) is a popular method for tailoring pre-trained large language models (LLMs), especially as the models' scale and the diversity of tasks increase.
CorpusBrain++: A Continual Generative Pre-Training Framework for Knowledge-Intensive Language Tasks
Very recently, a pre-trained generative retrieval model for KILTs, named CorpusBrain, was proposed and reached new state-of-the-art retrieval performance.
Query Augmentation by Decoding Semantics from Brain Signals
If the quality of the initially retrieved documents is low, then the effectiveness of query augmentation would be limited as well.
KnowTuning: Knowledge-aware Fine-tuning for Large Language Models
To address these problems, we propose a knowledge-aware fine-tuning (KnowTuning) method to improve fine-grained and coarse-grained knowledge awareness of LLMs.
Asking Multimodal Clarifying Questions in Mixed-Initiative Conversational Search
Therefore, we propose to add images to clarifying questions and formulate the novel task of asking multimodal clarifying questions in open-domain, mixed-initiative conversational search systems.
Perturbation-Invariant Adversarial Training for Neural Ranking Models: Improving the Effectiveness-Robustness Trade-Off
We decompose the robust ranking error into two components, i. e., a natural ranking error for effectiveness evaluation and a boundary ranking error for assessing adversarial robustness.
SARDINE: A Simulator for Automated Recommendation in Dynamic and Interactive Environments
Simulators can provide valuable insights for researchers and practitioners who wish to improve recommender systems, because they allow one to easily tweak the experimental setup in which recommender systems operate, and as a result lower the cost of identifying general trends and uncovering novel findings about the candidate methods.
Language Generation from Brain Recordings
Inspired by recent research that revealed associations between the brain and the large computational language models, we propose a generative language BCI that utilizes the capacity of a large language model (LLM) jointly with a semantic brain decoder to directly generate language from functional magnetic resonance imaging (fMRI) input.
Learning Robust Sequential Recommenders through Confident Soft Labels
CSRec contains a teacher module that generates high-quality and confident soft labels and a student module that acts as the target recommender and is trained on the combination of dense, soft labels and sparse, one-hot labels.
Hierarchical Forecasting at Scale
We implement our sparse hierarchical loss function within an existing forecasting model at bol, a large European e-commerce platform, resulting in an improved forecasting performance of 2% at the product level.
From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification
We argue that, rather than relevance, for FV we need to focus on the utility that a claim verifier derives from the retrieved evidence.
Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related Features
To address this limitation, recent studies enable generalization to an unseen target domain with only a few labeled examples using data augmentation techniques.
Predictive Uncertainty-based Bias Mitigation in Ranking
Prior work on bias mitigation often assumes that ranking scores, which correspond to the utility that a document holds for a user, can be accurately determined.
Continual Learning for Generative Retrieval over Dynamic Corpora
We put forward a novel Continual-LEarner for generatiVE Retrieval (CLEVER) model and make two major contributions to continual learning for GR: (i) To encode new documents into docids with low computational cost, we present Incremental Product Quantization, which updates a partial quantization codebook according to two adaptive thresholds; and (ii) To memorize new documents for querying without forgetting previous knowledge, we propose a memory-augmented learning mechanism, to form meaningful connections between old and new documents.
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method
The AREA task is meant to trick DR models into retrieving a target document that is outside the initial set of candidate documents retrieved by the DR model in response to a query.
Group Membership Bias
With group bias, the utility of the sensitive groups is under-estimated, hence, without correcting for this bias, a supposedly fair ranking is not truly fair.
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.
Answering Ambiguous Questions via Iterative Prompting
To provide feasible answers to an ambiguous question, one approach is to directly predict all valid answers, but this can struggle with balancing relevance and diversity.
RecFusion: A Binomial Diffusion Process for 1D Data for Recommendation
In this paper we propose RecFusion, which comprise a set of diffusion models for recommendation.
Distributional Reinforcement Learning with Dual Expectile-Quantile Regression
Motivated by the efficiency of $L_2$-based learning, we propose to jointly learn expectiles and quantiles of the return distribution in a way that allows efficient learning while keeping an estimate of the full distribution of returns.
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.
Query Performance Prediction: From Ad-hoc to Conversational Search
The QPP task is to predict the retrieval quality of a search system for a query without relevance judgments.
Iteratively Learning Representations for Unseen Entities with Inter-Rule Correlations
Recent work on knowledge graph completion (KGC) focused on learning embeddings of entities and relations in knowledge graphs.
Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment
Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target behavior such as purchases.
On the Impact of Outlier Bias on User Clicks
We therefore propose an outlier-aware click model that accounts for both outlier and position bias, called outlier-aware position-based model ( OPBM).
A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning
Learning task-specific retrievers that return relevant contexts at an appropriate level of semantic granularity, such as a document retriever, passage retriever, sentence retriever, and entity retriever, may help to achieve better performance on the end-to-end task.
Topic-oriented Adversarial Attacks against Black-box Neural Ranking Models
In this paper, we focus on a more general type of perturbation and introduce the topic-oriented adversarial ranking attack task against NRMs, which aims to find an imperceptible perturbation that can promote a target document in ranking for a group of queries with the same topic.
Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization
For the CLTR field, our novel exposure-based risk minimization method enables practitioners to adopt CLTR methods in a safer manner that mitigates many of the risks attached to previous methods.
An Offline Metric for the Debiasedness of Click Models
We prove that debiasedness is a necessary condition for recovering unbiased and consistent relevance scores and for the invariance of click prediction under covariate shift.
A Self-Correcting Sequential Recommender
STEAM first corrects an input item sequence by adjusting the misclicked and/or missed items.
Generative Slate Recommendation with Reinforcement Learning
Our findings suggest that representation learning using generative models is a promising direction towards generalizable RL-based slate recommendation.
Scene-centric vs. Object-centric Image-Text Cross-modal Retrieval: A Reproducibility Study
Additionally, we select two scene-centric datasets, and three object-centric datasets, and determine the relative performance of the selected models on these datasets.
Modeling Sequential Recommendation as Missing Information Imputation
To address the limitation of sequential recommenders with side information, we define a way to fuse side information and alleviate the problem of missing side information by proposing a unified task, namely the missing information imputation (MII), which randomly masks some feature fields in a given sequence of items, including item IDs, and then forces a predictive model to recover them.
Offline Evaluation for Reinforcement Learning-based Recommendation: A Critical Issue and Some Alternatives
In this paper, we argue that the paradigm commonly adopted for offline evaluation of sequential recommender systems is unsuitable for evaluating reinforcement learning-based recommenders.
Contrastive Learning Reduces Hallucination in Conversations
MixCL effectively reduces the hallucination of LMs in conversations and achieves the highest performance among LM-based dialogue agents in terms of relevancy and factuality.
Feature-Level Debiased Natural Language Understanding
We also propose a dynamic negative sampling strategy to capture the dynamic influence of biases by employing a bias-only model to dynamically select the most similar biased negative samples.
RADio -- Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations
In traditional recommender system literature, diversity is often seen as the opposite of similarity, and typically defined as the distance between identified topics, categories or word models.
Certified Robustness to Word Substitution Ranking Attack for Neural Ranking Models
A ranking model is said to be Certified Top-$K$ Robust on a ranked list when it is guaranteed to keep documents that are out of the top $K$ away from the top $K$ under any attack.
Intersection of Parallels as an Early Stopping Criterion
Using this result, we propose to train two parallel instances of a linear model, initialized with different random seeds, and use their intersection as a signal to detect overfitting.
Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users
In this paper, we report on ongoing work regarding (i) the development of an AI system for flagging and explaining low-quality medical images in real-time, (ii) an interview study to understand the explanation needs of stakeholders using the AI system at OurCompany, and, (iii) a longitudinal user study design to examine the effect of including explanations on the workflow of the technicians in our clinics.
Debiasing Learning for Membership Inference Attacks Against Recommender Systems
To address the above limitations, we propose a Debiasing Learning for Membership Inference Attacks against recommender systems (DL-MIA) framework that has four main components: (1) a difference vector generator, (2) a disentangled encoder, (3) a weight estimator, and (4) an attack model.
A Simulation Environment and Reinforcement Learning Method for Waste Reduction
We frame inventory restocking as a new reinforcement learning task that exhibits stochastic behavior conditioned on the agent's actions, making the environment partially observable.
Distributional Reinforcement Learning
reinforcement-learning
+1
Fairness of Exposure in Light of Incomplete Exposure Estimation
In this work, we discuss how to approach fairness of exposure in cases where the policy contains rankings of which, due to inter-item dependencies, we cannot reliably estimate the exposure distribution.
State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study
In response to these shortcomings, we reproduce and expand on the existing comparison of attention-based state encoders (1) in the publicly available debiased RL4Rec SOFA simulator with (2) a different RL method, (3) more state encoders, and (4) a different dataset.
A Simple Contrastive Learning Objective for Alleviating Neural Text Degeneration
The cross-entropy objective has proved to be an all-purpose training objective for autoregressive language models (LMs).
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.
Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking
Different from PL, where pointwise logits are used as the distribution parameters, in PPG pairwise inversion probabilities together with a reference permutation construct the distribution.
Understanding User Satisfaction with Task-oriented Dialogue Systems
What is the influence of user experience on the user satisfaction rating of TDS as opposed to, or in addition to, utility?
Summarization with Graphical Elements
Motivated from these two angles, we propose a new task: summarization with graphical elements, and we verify that these summaries are helpful for a critical mass of people.
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.
PRADA: Practical Black-Box Adversarial Attacks against Neural Ranking Models
We focus on the decision-based black-box attack setting, where the attackers cannot directly get access to the model information, but can only query the target model to obtain the rank positions of the partial retrieved list.
Metaphorical User Simulators for Evaluating Task-oriented Dialogue Systems
Employing existing user simulators to evaluate TDSs is challenging as user simulators are primarily designed to optimize dialogue policies for TDSs and have limited evaluation capabilities.
Do Lessons from Metric Learning Generalize to Image-Caption Retrieval?
Recent progress in metric learning has given rise to new loss functions that outperform the triplet loss on tasks such as image retrieval and representation learning.
Extending CLIP for Category-to-image Retrieval in E-commerce
One aspect of this data is a category tree that is being used in search and recommendation.
Understanding and Mitigating the Effect of Outliers in Fair Ranking
We formalize outlierness in a ranking, show that outliers are present in realistic datasets, and present the results of an eye-tracking study, showing that users scanning order and the exposure of items are influenced by the presence of outliers.
Parameter Efficient Deep Probabilistic Forecasting
However, these methods require a large number of parameters to be learned, which imposes high memory requirements on the computational resources for training such models.
It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic
We theoretically show that in a dynamic scenario in which both the selection bias and user preferences are dynamic, existing debiasing methods are no longer unbiased.
Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence
In this work, we explain the setup for a technical, graduate-level course on Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence (FACT-AI) at the University of Amsterdam, which teaches FACT-AI concepts through the lens of reproducibility.
A Next Basket Recommendation Reality Check
We propose a set of metrics that measure the repeat/explore ratio and performance of NBR models.
ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues
(1) there is no dataset with large-scale medical dialogues that covers multiple medical services and contains fine-grained medical labels (i. e., intents, actions, slots, values), and (2) there is no set of established benchmarks for MDSs for multi-domain, multi-service medical dialogues.
sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification
We propose a loss function, sigmoidF1, which is an approximation of the F1 score that (1) is smooth and tractable for stochastic gradient descent, (2) naturally approximates a multilabel metric, and (3) estimates label propensities and label counts.
Mixture-Based Correction for Position and Trust Bias in Counterfactual Learning to Rank
Affine correction (AC) is a generalization of IPS that corrects for position bias and trust bias.
A Human-machine Collaborative Framework for Evaluating Malevolence in Dialogues
HMCEval casts dialogue evaluation as a sample assignment problem, where we need to decide to assign a sample to a human or a machine for evaluation.
News Article Retrieval in Context for Event-centric Narrative Creation
Given an incomplete narrative that specifies a main event and a context, we aim to retrieve news articles that discuss relevant events that would enable the continuation of the narrative.
Learning to Ask Conversational Questions by Optimizing Levenshtein Distance
Conversational Question Simplification (CQS) aims to simplify self-contained questions into conversational ones by incorporating some conversational characteristics, e. g., anaphora and ellipsis.
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.
Improving Transformer-based Sequential Recommenders through Preference Editing
By doing so, the SR model is able to learn how to identify common and unique user preferences, and thereby do better user preference extraction and representation.
Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression
We propose Probabilistic Gradient Boosting Machines (PGBM), a method to create probabilistic predictions with a single ensemble of decision trees in a computationally efficient manner.
Wizard of Search Engine: Access to Information Through Conversations with Search Engines
(2) We release a benchmark dataset, called wizard of search engine (WISE), which allows for comprehensive and in-depth research on all aspects of CIS.
Semi-Supervised Variational Reasoning for Medical Dialogue Generation
We propose an end-to-end variational reasoning approach to medical dialogue generation.
Cross-Domain Contract Element Extraction with a Bi-directional Feedback Clause-Element Relation Network
First, contract elements are far more fine-grained than named entities, which hinders the transfer of extractors.
Cross-Domain Named Entity Recognition
named-entity-recognition
+4
Simulating User Satisfaction for the Evaluation of Task-oriented Dialogue Systems
The purpose of the task is to increase the evaluation power of user simulations and to make the simulation more human-like.
Improving Response Quality with Backward Reasoning in Open-domain Dialogue Systems
The proposed backward reasoning step pushes the model to produce more informative and coherent content because the forward generation step's output is used to infer the dialogue context in the backward direction.
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.
A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles
We find that CoMemNN is able to enrich user profiles effectively, which results in an improvement of 3. 06% in terms of response selection accuracy compared to state-of-the-art methods.
Robust Generalization and Safe Query-Specialization in Counterfactual Learning to Rank
We introduce the Generalization and Specialization (GENSPEC) algorithm, a robust feature-based counterfactual LTR method that pursues per-query memorization when it is safe to do so.
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks
In this work, we propose a method for generating CF explanations for GNNs: the minimal perturbation to the input (graph) data such that the prediction changes.
Advances and Challenges in Conversational Recommender Systems: A Survey
In this paper, we provide a systematic review of the techniques used in current CRSs.
Abstractive Opinion Tagging
In this paper, we propose the abstractive opinion tagging task, where systems have to automatically generate a ranked list of opinion tags that are based on, but need not occur in, a given set of user-generated reviews.
Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers
In this paper, we focus on purchase prediction for both anonymous and identified sessions on an e-commerce platform.
What Makes a Good and Useful Summary? Incorporating Users in Automatic Summarization Research
Motivated by our findings, we present ways to mitigate this mismatch in future research on automatic summarization: we propose research directions that impact the design, the development and the evaluation of automatically generated summaries.
Unifying Online and Counterfactual Learning to Rank
With the introduction of the intervention-aware estimator, we aim to bridge the online/counterfactual LTR division as it is shown to be highly effective in both online and counterfactual scenarios.
Conversational Browsing
How can we better understand the mechanisms behind multi-turn information seeking dialogues?
Mixed Information Flow for Cross-domain Sequential Recommendations
One of the key challenges in cross-domain sequential recommendation is to grasp and transfer the flow of information from multiple domains so as to promote recommendations in all domains.
Guided Dialogue Policy Learning without Adversarial Learning in the Loop
Reinforcement learning methods have emerged as a popular choice for training an efficient and effective dialogue policy.
Star Graph Neural Networks for Session-based Recommendation
The proposed SGNN-HN applies a star graph neural network (SGNN) to model the complex transition relationship between items in an ongoing session.
Ranked #1 on
Session-Based Recommendations
on yoochoose1/64
Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems
Then, the traditional multi-label classification solution for dialogue policy learning is extended by adding dense layers to improve the dialogue agent performance.
When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank
Our main contribution is a new estimator based on affine corrections: it both reweights clicks and penalizes items displayed on ranks with high trust bias.
Detecting and Classifying Malevolent Dialogue Responses: Taxonomy, Data and Methodology
In this paper, we define the task of Malevolent Dialogue Response Detection and Classification (MDRDC).
Diversifying Task-oriented Dialogue Response Generation with Prototype Guided Paraphrasing
Instead of generating a response from scratch, P2-Net generates system responses by paraphrasing template-based responses.
Attribute-aware Diversification for Sequential Recommendations
Users prefer diverse recommendations over homogeneous ones.
Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking
LogOpt turns the counterfactual approach - which is indifferent to the logging policy - into an online approach, where the algorithm decides what rankings to display.
A Comparison of Supervised Learning to Match Methods for Product Search
As recent learning to match methods have made important advances in bridging the vocabulary gap for these traditional IR areas, we investigate their potential in the context of product search.
Pre-Trained Models for Heterogeneous Information Networks
We propose a self-supervised pre-training and fine-tuning framework, PF-HIN, to capture the features of a heterogeneous information network.
Optimizing Interactive Systems via Data-Driven Objectives
Effective optimization is essential for real-world interactive systems to provide a satisfactory user experience in response to changing user behavior.
Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank
Unbiased CLTR requires click propensities to compensate for the difference between user clicks and true relevance of search results via IPS.
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.
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.
Accelerated Convergence for Counterfactual Learning to Rank
Counterfactual Learning to Rank (LTR) algorithms learn a ranking model from logged user interactions, often collected using a production system.
Policy-Aware Unbiased Learning to Rank for Top-k Rankings
We prove that the policy-aware estimator is unbiased if every relevant item has a non-zero probability to appear in the top-k ranking.
Rethinking Item Importance in Session-based Recommendation
We employ a modified self-attention mechanism to estimate item importance in a session, which is then used to predict user's long-term preference.
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.
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.
Guided Dialog Policy Learning without Adversarial Learning in the Loop
Reinforcement Learning (RL) methods have emerged as a popular choice for training an efficient and effective dialogue policy.
TLDR: Token Loss Dynamic Reweighting for Reducing Repetitive Utterance Generation
We hypothesize that the deeper reason is that in the training corpora, there are hard tokens that are more difficult for a generative model to learn than others and, once learning has finished, hard tokens are still under-learned, so that repetitive generations are more likely to happen.
Safe Exploration for Optimizing Contextual Bandits
Our experiments using text classification and document retrieval confirm the above by comparing SEA (and a boundless variant called BSEA) to online and offline learning methods for contextual bandit problems.
Conversations with Documents. An Exploration of Document-Centered Assistance
Our contributions are three-fold: (1) We first present a survey to understand the space of document-centered assistance and the capabilities people expect in this scenario.
Bidirectional Scene Text Recognition with a Single Decoder
We introduce the bidirectional Scene Text Transformer (Bi-STET), a novel bidirectional STR method with a single decoder for bidirectional text decoding.
Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity
Relevance ranking aims at building a ranked list sorted in decreasing order of item relevance, while result diversification focuses on generating a ranked list of items that covers a broad range of topics.
FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles
Model interpretability has become an important problem in machine learning (ML) due to the increased effect that algorithmic decisions have on humans.
Retrospective and Prospective Mixture-of-Generators for Task-oriented Dialogue Response Generation
We propose a novel mixture-of-generators network (MoGNet) for DRG, where we assume that each token of a response is drawn from a mixture of distributions.
Understanding Multi-Head Attention in Abstractive Summarization
Finally, we find that relative positions heads seem integral to summarization performance and persistently remain after pruning.
Meta Matrix Factorization for Federated Rating Predictions
Given a user, we first obtain a collaborative vector by collecting useful information with a collaborative memory module.
How to Not Measure Disentanglement
We conclude that existing metrics of disentanglement were created to reflect different characteristics of disentanglement and do not satisfy two basic desirable properties: (1) assign a high score to representations that are disentangled according to the definition; and (2) assign a low score to representations that are entangled according to the definition.
Parallel Split-Join Networks for Shared-account Cross-domain Sequential Recommendations
We study sequential recommendation in a particularly challenging context, in which multiple individual users share asingle account (i. e., they have a shared account) and in which user behavior is available in multiple domains (i. e., recommendations are cross-domain).
Pre-train, Interact, Fine-tune: A Novel Interaction Representation for Text Classification
We introduce the concept of interaction and propose a two-perspective interaction representation, that encapsulates a local and a global interaction representation.
Improving End-to-End Sequential Recommendations with Intent-aware Diversification
Then, we design an Intent-aware Diversity Promoting (IDP) loss to supervise the learning of the IIM module and force the model to take recommendation diversity into consideration during training.
Thinking Globally, Acting Locally: Distantly Supervised Global-to-Local Knowledge Selection for Background Based Conversation
Given a conversational context and background knowledge, we first learn a topic transition vector to encode the most likely text fragments to be used in the next response, which is then used to guide the local KS at each decoding timestamp.
Improving Outfit Recommendation with Co-supervision of Fashion Generation
FARM improves visual understanding by incorporating the supervision of generation loss, which we hypothesize to be able to better encode aesthetic information.
Message Passing for Complex Question Answering over Knowledge Graphs
We propose a novel approach for complex KGQA that uses unsupervised message passing, which propagates confidence scores obtained by parsing an input question and matching terms in the knowledge graph to a set of possible answers.
RefNet: A Reference-aware Network for Background Based Conversation
In this paper, we propose a Reference-aware Network (RefNet) to address the two issues.
Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting
Given a large error, MC-BRP determines (1) feature values that would result in a reasonable prediction, and (2) general trends between each feature and the target, both based on Monte Carlo simulations.
Unbiased Learning to Rank: Counterfactual and Online Approaches
Through randomization the effect of different types of bias can be removed from the learning process.
To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions
At the moment, two methodologies for dealing with bias prevail in the field of LTR: counterfactual methods that learn from historical data and model user behavior to deal with biases; and online methods that perform interventions to deal with bias but use no explicit user models.
Proceedings of FACTS-IR 2019
The proceedings list for the program of FACTS-IR 2019, the Workshop on Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval held at SIGIR 2019.
A Modular Task-oriented Dialogue System Using a Neural Mixture-of-Experts
We propose a neural Modular Task-oriented Dialogue System(MTDS) framework, in which a few expert bots are combined to generate the response for a given dialogue context.
Joint Neural Collaborative Filtering for Recommender Systems
Deep feature learning extracts feature representations of users and items with a deep learning architecture based on a user-item rating matrix.
Explaining Predictions from Tree-based Boosting Ensembles
Understanding how "black-box" models arrive at their predictions has sparked significant interest from both within and outside the AI community.
Do Transformer Attention Heads Provide Transparency in Abstractive Summarization?
We investigate whether distributions calculated by different attention heads in a transformer architecture can be used to improve transparency in the task of abstractive summarization.
SEntNet: Source-aware Recurrent Entity Network for Dialogue Response Selection
Obtaining key information from a complex, long dialogue context is challenging, especially when different sources of information are available, e. g., the user's utterances, the system's responses, and results retrieved from a knowledge base (KB).
Improving Background Based Conversation with Context-aware Knowledge Pre-selection
The latter generate responses thatare natural but not necessarily effective in leveraging background knowledge.
Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model
We consider the problem of identifying the K most attractive items and propose cascading non-stationary bandits, an online learning variant of the cascading model, where a user browses a ranked list from top to bottom and clicks on the first attractive item.
ViTOR: Learning to Rank Webpages Based on Visual Features
The visual appearance of a webpage carries valuable information about its quality and can be used to improve the performance of learning to rank (LTR).
Improving Neural Response Diversity with Frequency-Aware Cross-Entropy Loss
Specifically, we first analyze the influence of the commonly used Cross-Entropy (CE) loss function, and find that the CE loss function prefers high-frequency tokens, which results in low-diversity responses.
QRFA: A Data-Driven Model of Information-Seeking Dialogues
Understanding the structure of interaction processes helps us to improve information-seeking dialogue systems.
MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation
Our main finding is that for large-scale Condorcet ranker evaluation problems, MergeDTS outperforms the state-of-the-art dueling bandit algorithms.
Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning
The performance of adversarial dialogue generation models relies on the quality of the reward signal produced by the discriminator.
RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation
RepeatNet integrates a regular neural recommendation approach in the decoder with a new repeat recommendation mechanism that can choose items from a user's history and recommends them at the right time.
HiTR: Hierarchical Topic Model Re-estimation for Measuring Topical Diversity of Documents
For measuring topical diversity of text documents, our HiTR approach improves over the state-of-the-art measured on PubMed dataset.
Differentiable Unbiased Online Learning to Rank
Instead, its gradient is based on inferring preferences between document pairs from user clicks and can optimize any differentiable model.
Why are Sequence-to-Sequence Models So Dull? Understanding the Low-Diversity Problem of Chatbots
Sequence-to-sequence (Seq2Seq) models have been shown to be very effective for response generation.
Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation
The generated comments can be regarded as explanations for the recommendation results.
Incremental Sparse Bayesian Ordinal Regression
Ordinal Regression (OR) aims to model the ordering information between different data categories, which is a crucial topic in multi-label learning.
Measuring Semantic Coherence of a Conversation
Conversational systems have become increasingly popular as a way for humans to interact with computers.
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback
In this paper, we study the problem of safe online learning to re-rank, where user feedback is used to improve the quality of displayed lists.
Open Domain Suggestion Mining: Problem Definition and Datasets
We first present an annotation study, and based on our observations propose a formal task definition and annotation procedure for creating benchmark datasets for suggestion mining.
Faithfully Explaining Rankings in a News Recommender System
There is an increasing demand for algorithms to explain their outcomes.
Ranking for Relevance and Display Preferences in Complex Presentation Layouts
Existing learning to rank methods cannot handle such complex ranking settings as they assume that the display order is known beforehand.
Weakly-supervised Contextualization of Knowledge Graph Facts
KG fact contextualization is the task of augmenting a given KG fact with additional and useful KG facts.
Finding Influential Training Samples for Gradient Boosted Decision Trees
We address the problem of finding influential training samples for a particular case of tree ensemble-based models, e. g., Random Forest (RF) or Gradient Boosted Decision Trees (GBDT).
Learning Data-Driven Objectives to Optimize Interactive Systems
Effective optimization is essential for interactive systems to provide a satisfactory user experience.
Deep Learning with Logged Bandit Feedback
We propose a new output layer for deep neural networks that permits the use of logged contextual bandit feedback for training.
Attentive Memory Networks: Efficient Machine Reading for Conversational Search
We argue that the process of building a representation of the conversation can be framed as a machine reading task, where an automated system is presented with a number of statements about which it should answer questions.
Conversational Exploratory Search via Interactive Storytelling
In this paper we investigate the affordances of interactive storytelling as a tool to enable exploratory search within the framework of a conversational interface.
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.
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.
Share your Model instead of your Data: Privacy Preserving Mimic Learning for Ranking
Deep neural networks have become a primary tool for solving problems in many fields.
Thread Reconstruction in Conversational Data using Neural Coherence Models
Discussion forums are an important source of information.
Modeling Label Ambiguity for Neural List-Wise Learning to Rank
List-wise learning to rank methods are considered to be the state-of-the-art.
Neural Networks for Information Retrieval
Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them.
Semantic Entity Retrieval Toolkit
Unsupervised learning of low-dimensional, semantic representations of words and entities has recently gained attention.
The Birth of Collective Memories: Analyzing Emerging Entities in Text Streams
We do so by tracking entities that emerge in public discourse, that is, in online text streams such as social media and news streams, before they are incorporated into Wikipedia, which, we argue, can be viewed as an online place for collective memory.
Pyndri: a Python Interface to the Indri Search Engine
We introduce pyndri, a Python interface to the Indri search engine.
Large-scale Validation of Counterfactual Learning Methods: A Test-Bed
The ability to perform effective off-policy learning would revolutionize the process of building better interactive systems, such as search engines and recommendation systems for e-commerce, computational advertising and news.
Getting Started with Neural Models for Semantic Matching in Web Search
We detail the required background and terminology, a taxonomy grouping the rapidly growing body of work in the area, and then survey work on neural models for semantic matching in the context of three tasks: query suggestion, ad retrieval, and document retrieval.
Probabilistic Feature Selection and Classification Vector Machine
The proposed method, called probabilistic feature selection and classification vector machine (PFCVMLP ), is able to simultaneously select relevant features and samples for classification tasks.
Incorporating Clicks, Attention and Satisfaction into a Search Engine Result Page Evaluation Model
In this paper we propose a model of user behavior on a SERP that jointly captures click behavior, user attention and satisfaction, the CAS model, and demonstrate that it gives more accurate predictions of user actions and self-reported satisfaction than existing models based on clicks alone.
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.
Lexical Query Modeling in Session Search
Lexical query modeling has been the leading paradigm for session search.
Unsupervised, Efficient and Semantic Expertise Retrieval
We compare our model to state-of-the-art unsupervised statistical vector space and probabilistic generative approaches.
Siamese CBOW: Optimizing Word Embeddings for Sentence Representations
We present the Siamese Continuous Bag of Words (Siamese CBOW) model, a neural network for efficient estimation of high-quality sentence embeddings.
Computing Web-scale Topic Models using an Asynchronous Parameter Server
Topic models such as Latent Dirichlet Allocation (LDA) have been widely used in information retrieval for tasks ranging from smoothing and feedback methods to tools for exploratory search and discovery.
Active Learning for Entity Filtering in Microblog Streams
Monitoring the reputation of entities such as companies or brands in microblog streams (e. g., Twitter) starts by selecting mentions that are related to the entity of interest.
Copeland Dueling Bandits
A version of the dueling bandit problem is addressed in which a Condorcet winner may not exist.
Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem
This paper proposes a new method for the K-armed dueling bandit problem, a variation on the regular K-armed bandit problem that offers only relative feedback about pairs of arms.
