Ludovica Pannitto


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Recurrent babbling: evaluating the acquisition of grammar from limited input data
Ludovica Pannitto | Aurélie Herbelot
Proceedings of the 24th Conference on Computational Natural Language Learning

Recurrent Neural Networks (RNNs) have been shown to capture various aspects of syntax from raw linguistic input. In most previous experiments, however, learning happens over unrealistic corpora, which do not reflect the type and amount of data a child would be exposed to. This paper remedies this state of affairs by training an LSTM over a realistically sized subset of child-directed input. The behaviour of the network is analysed over time using a novel methodology which consists in quantifying the level of grammatical abstraction in the model’s generated output (its ‘babbling’), compared to the language it has been exposed to. We show that the LSTM indeed abstracts new structures as learning proceeds.

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Are Word Embeddings Really a Bad Fit for the Estimation of Thematic Fit?
Emmanuele Chersoni | Ludovica Pannitto | Enrico Santus | Alessandro Lenci | Chu-Ren Huang
Proceedings of the 12th Language Resources and Evaluation Conference

While neural embeddings represent a popular choice for word representation in a wide variety of NLP tasks, their usage for thematic fit modeling has been limited, as they have been reported to lag behind syntax-based count models. In this paper, we propose a complete evaluation of count models and word embeddings on thematic fit estimation, by taking into account a larger number of parameters and verb roles and introducing also dependency-based embeddings in the comparison. Our results show a complex scenario, where a determinant factor for the performance seems to be the availability to the model of reliable syntactic information for building the distributional representations of the roles.


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FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering
Giuseppe Attardi | Antonio Carta | Federico Errica | Andrea Madotto | Ludovica Pannitto
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

In this paper we present ThReeNN, a model for Community Question Answering, Task 3, of SemEval-2017. The proposed model exploits both syntactic and semantic information to build a single and meaningful embedding space. Using a dependency parser in combination with word embeddings, the model creates sequences of inputs for a Recurrent Neural Network, which are then used for the ranking purposes of the Task. The score obtained on the official test data shows promising results.