Add to ORKGInternational audienceIn this work, our aim is to provide a structured answer in natural language to a complex information need. Particularly, we envision using generative models from the perspective of data-to-text generation. We propose the use of a content selection and planning pipeline which aims at structuring the answer by generating intermediate plans. The experimental evaluation is performed using the TREC Complex Answer Retrieval (CAR) dataset. We evaluate both the generated answer and its corresponding structure and show the effectiveness of planning-based models in comparison to a text-to-text model. This work has been published at ECIR 2022
The ability to convey relevant and faithful information is critical for many tasks in conditional ge...
In this thesis we examine ways of conditionally generating document-scale natural language text give...
Using a computer to answer questions has been a human dream since the beginning of the digital era. ...
International audienceIn this work, our aim is to provide a structured answer in natural language to...
Recent advances in data-to-text generation have led to the use of large-scale datasets and neural ne...
Providing answers to complex information needs is a challenging task. The new TREC Complex Answer Re...
The problem of Data-to-Text Generation (D2T) is usually solved using a modular approach by breaking ...
Data-to-text systems are powerful in generating reports from data automatically and thus they simpli...
In this chapter, we describe our efforts in text-to-text generation within the IMOGEN project. In pa...
Recent trends in Question Answering (QA) have led to numerous studies focusing on pre-senting answer...
International audienceGenerative models for open domain question answering have proven to be competi...
Users are often faced with complex information needs that are not easily represented as a single que...
In this thesis, we consider the task of data-to-text generation, which takes non-linguistic structu...
In a language generation system, a content planner selects which elements must be in-cluded in the o...
If a generation system is to produce text in response to a given communicative goal, it must be able...
The ability to convey relevant and faithful information is critical for many tasks in conditional ge...
In this thesis we examine ways of conditionally generating document-scale natural language text give...
Using a computer to answer questions has been a human dream since the beginning of the digital era. ...
International audienceIn this work, our aim is to provide a structured answer in natural language to...
Recent advances in data-to-text generation have led to the use of large-scale datasets and neural ne...
Providing answers to complex information needs is a challenging task. The new TREC Complex Answer Re...
The problem of Data-to-Text Generation (D2T) is usually solved using a modular approach by breaking ...
Data-to-text systems are powerful in generating reports from data automatically and thus they simpli...
In this chapter, we describe our efforts in text-to-text generation within the IMOGEN project. In pa...
Recent trends in Question Answering (QA) have led to numerous studies focusing on pre-senting answer...
International audienceGenerative models for open domain question answering have proven to be competi...
Users are often faced with complex information needs that are not easily represented as a single que...
In this thesis, we consider the task of data-to-text generation, which takes non-linguistic structu...
In a language generation system, a content planner selects which elements must be in-cluded in the o...
If a generation system is to produce text in response to a given communicative goal, it must be able...
The ability to convey relevant and faithful information is critical for many tasks in conditional ge...
In this thesis we examine ways of conditionally generating document-scale natural language text give...
Using a computer to answer questions has been a human dream since the beginning of the digital era. ...