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Works96 from public data
- TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance652
This work extracts samples from real financial reports to build a new large-scale QA dataset containing both Tabular And Textual data, named TAT-QA, where numerical reasoning is usually required to infer the answer, such as addition, subtraction, multiplication, division, counting, comparison/sorting, and the compositions.
- Advances and challenges in conversational recommender systems: A survey396
A systematic review of the techniques used in current conversational recommender systems, and summarizes the key challenges of developing CRSs in five directions: Question-based user preference elicitation, multi-turn conversational recommendation strategies, dialogue understanding and generation, evaluation and user simulation, and exploitation-exploration trade-offs.
- Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems353
This work proposes a new CRS framework named Estimation"Action" Reflection, or EAR, which consists of three stages to better converse with users, and conducts extensive experiments on two datasets from Yelp and LastFM to demonstrate significant improvements over the state-of-the-art method CRM.
- Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures349
A novel, holistic, extendable framework based on a single sequence-to-sequence (seq2seq) model which can be optimized with supervised or reinforcement learning is proposed which significantly outperforms state- of-the-art pipeline-based methods on large datasets and retains a satisfactory entity match rate on out-of-vocabulary (OOV) cases where pipeline-designed competitors totally fail.
- KuaiRec303
A fully-observed dataset from Kuaishou's online environment, where almost all 1,411 users have been exposed to all 3,327 items, and a preliminary analysis of how the two factors - data density and exposure bias - affect the evaluation results of multi-round conversational recommendation.
- KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos233
KuaiRand is presented, an unbiased sequential recommendation dataset containing millions of intervened interactions on randomly exposed videos, collected from the video-sharing mobile App, Kuaishou, to enable the research of advanced debiasing large-scale recommendation scenarios for the first time.
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- CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System141
A counterfactual interactive recommender system (CIRS) that augments offline reinforcement learning (offline RL) with causal inference that shows the effectiveness of CIRS in bursting filter bubbles and achieving long-term success in interactive recommendation.
- Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-start Users138
The Conversational Thompson Sampling (ConTS) model holistically solves all questions in conversational recommendation by choosing the arm with the maximal reward to play and seamlessly unify attributes and items in the same arm space and achieve their EE trade-offs automatically using the framework ofThompson Sampling.
- Re-examining the Role of Schema Linking in Text-to-SQL130
This work provides a schema linking corpus based on the Spider text-to-SQL dataset, and builds a simple BERT-based baseline to perform a data-driven study on schema linking, finding when schema linking is done well, SLSQL demonstrates good performance on Spider despite its structural simplicity.
- Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure125
The Molweni dataset is presented, a machine reading comprehension (MRC) dataset with discourse structure built over multiparty dialog with large-scale annotations in a modified Segmented Discourse Representation Theory (SDRT; Asher et al., 2016) style.
- Towards Complex Document Understanding By Discrete Reasoning121
A novel model named MHST is developed that takes into account the information in multi-modalities to intelligently address different types of questions with corresponding strategies, i.e., extraction or reasoning.
- Proactive Conversational AI: A Comprehensive Survey of Advancements and Opportunities90
A comprehensive review of various prominent problems and advanced designs for implementing proactivity into different types of dialogue systems, including open-domain dialogues, task-oriented dialogues, and information-seeking dialogues are provided.
- Conversational Recommendation: Formulation, Methods, and Evaluation89
This tutorial covers four emerging directions of conversational recommender systems, providing a review of existing approaches and progress on the topic, and provides take-aways to practitioners to build their own systems.
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- A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems80
This work proposes a novel Unified MultI-goal conversational recommeNDer system (UniMIND), which unify these four tasks with different formulations into the same sequence-to-sequence paradigm and achieves state of the art performance on all tasks with a unified model.
- Towards Analyzing and Understanding the Limitations of DPO: A Theoretical Perspective77
By analyzing the gradient vector field of the DPO loss function, it is found that the DPO loss function decreases the probability of producing human dispreferred data at a faster rate than it increases the probability of producing preferred data.
- Explicit State Tracking with Semi-Supervisionfor Neural Dialogue Generation59
This paper proposes an encoder-decoder architecture, named CopyFlowNet, to represent an explicit dialogue state with a probabilistic distribution over the vocabulary space, and applies a posterior regularization strategy to integrate indirect supervision in the training procedure.
- Tailor: A Soft-Prompt-Based Approach to Attribute-Based Controlled Text Generation57
The proposed Tailor represents each attribute as a pre- trained continuous vector i.e., single-attribute prompt, which guides the generation of a fixed pre-trained language model (PLM) to satisfy aPre-specified attribute.
- SWIM: A Simple Word Interaction Model for Implicit Discourse Relation Recognition54
This work demonstrates that word-weighted averaging can encode argument representation which can be incorporated with word pair information efficiently and saves an order of magnitude in parameters and eschewing the recurrent structure.
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- Linguistic Properties Matter for Implicit Discourse Relation Recognition: Combining Semantic Interaction, Topic Continuity and Attribution46
This model is built from first principles, analyzing the linguistic properties of the individual top-level Penn Discourse Treebank styled implicit discourse relations: Comparison, Contingency and Expansion to achieve a new state-of-the-art performance level.
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- Rethinking Conversational Agents in the Era of LLMs: Proactivity, Non-collaborativity, and Beyond39
This tutorial will introduce the recent advances on the design of agent’s awareness of goals in a wide range of Conversational systems, including proactive, non-collaborative, and multi-goal conversational systems.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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