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Found 8 papers, 3 papers with code
Exploring the Value of Multi-View Learning for Session-Aware Query Representation
Recent years have witnessed a growing interest towards learning distributed query representations that are able to capture search intent semantics.
Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at Each Single-Hop?
Despite the success of state-of-the-art pre-trained language models (PLMs) on a series of multi-hop reasoning tasks, they still suffer from their limited abilities to transfer learning from simple to complex tasks and vice-versa.
Probing Pretrained Language Models with Hierarchy Properties
In this work, we propose a task-agnostic evaluation method able to evaluate to what extent PLMs can capture complex taxonomy relations, such as ancestors and siblings.
Does Structure Matter? Leveraging Data-to-Text Generation for Answering Complex Information Needs
In this work, our aim is to provide a structured answer in natural language to a complex information need.
Studying Catastrophic Forgetting in Neural Ranking Models
Several deep neural ranking models have been proposed in the recent IR literature.
DSRIM: A Deep Neural Information Retrieval Model Enhanced by a Knowledge Resource Driven Representation of Documents
The state-of-the-art solutions to the vocabulary mismatch in information retrieval (IR) mainly aim at leveraging either the relational semantics provided by external resources or the distributional semantics, recently investigated by deep neural approaches.
Toward a Deep Neural Approach for Knowledge-Based IR
With this in mind, we argue that embedding KBs within deep neural architectures supporting documentquery matching would give rise to fine-grained latent representations of both words and their semantic relations.
