• Corpus ID: 1498636

Iterative Multi-document Neural Attention for Multiple Answer Prediction

@inproceedings{Greco2017IterativeMN,
  title={Iterative Multi-document Neural Attention for Multiple Answer Prediction},
  author={Claudio Greco and Alessandro Suglia and Pierpaolo Basile and Gaetano Rossiello and Giovanni Semeraro},
  booktitle={URANIA@AI*IA},
  year={2017}
}
People have information needs of varying complexity, which can be solved by an intelligent agent able to answer questions formulated in a proper way, eventually considering user context and preferences. [] Key Result After assessing the performance of the model on both tasks, we try to define the long-term goal of a conversational recommender system able to interact using natural language and to support users in their information seeking processes in a personalized way.
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References

SHOWING 1-10 OF 27 REFERENCES

Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

This work argues for the usefulness of a set of proxy tasks that evaluate reading comprehension via question answering, and classify these tasks into skill sets so that researchers can identify (and then rectify) the failings of their systems.

Text Understanding with the Attention Sum Reader Network

A new, simple model is presented that uses attention to directly pick the answer from the context as opposed to computing the answer using a blended representation of words in the document as is usual in similar models, making the model particularly suitable for question-answering problems where the answer is a single word from the document.

Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems

This work proposes a suite of new tasks that test the ability of models to answer factual questions, provide personalization, carry short conversations about the two, and finally to perform on natural dialogs from Reddit.

Active Learning for Recommender Systems

  • R. Karimi
  • Computer Science
    KI - Künstliche Intelligenz
  • 2014
The aim of this dissertation is to take inspiration from the literature of active learning for classification (regression) problems and develop new methods for the new-user problem in recommender systems.

Improving recommender systems with adaptive conversational strategies

It is shown that the optimal strategy is different from the fixed one, and supports more effective and efficient interaction sessions, and allows conversational systems to autonomously improve a fixed strategy and eventually learn a better one using reinforcement learning techniques.

SQuAD: 100,000+ Questions for Machine Comprehension of Text

A strong logistic regression model is built, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%).

Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neural Networks

A comprehensive experimental evaluation conducted on state-of-the-art datasets showed a significant improvement over all the baselines taken into account.

Semantics-Aware Content-Based Recommender Systems

  • P. Lops
  • Computer Science
    Recommender Systems Handbook
  • 2014
This chapter presents a comprehensive survey of semantic representations of items and user profiles that attempt to overcome the main problems of the simpler approaches based on keywords and proposes a classification of semantic approaches into top-down and bottom-up.

Memory Networks

This work describes a new class of learning models called memory networks, which reason with inference components combined with a long-term memory component; they learn how to use these jointly.

Iterative Alternating Neural Attention for Machine Reading

This work proposes a novel neural attention architecture to tackle machine comprehension tasks, such as answering Cloze-style queries with respect to a document that outperforms state-of-the-art baselines in standard machine comprehension benchmarks such as CNN news articles and the Children’s Book Test dataset.