Marc Tanti


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Visually grounded generation of entailments from premises
Somayeh Jafaritazehjani | Albert Gatt | Marc Tanti
Proceedings of the 12th International Conference on Natural Language Generation

Natural Language Inference (NLI) is the task of determining the semantic relationship between a premise and a hypothesis. In this paper, we focus on the generation of hypotheses from premises in a multimodal setting, to generate a sentence (hypothesis) given an image and/or its description (premise) as the input. The main goals of this paper are (a) to investigate whether it is reasonable to frame NLI as a generation task; and (b) to consider the degree to which grounding textual premises in visual information is beneficial to generation. We compare different neural architectures, showing through automatic and human evaluation that entailments can indeed be generated successfully. We also show that multimodal models outperform unimodal models in this task, albeit marginally


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Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions
Albert Gatt | Marc Tanti | Adrian Muscat | Patrizia Paggio | Reuben A Farrugia | Claudia Borg | Kenneth P Camilleri | Michael Rosner | Lonneke van der Plas
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Grounded Textual Entailment
Hoa Trong Vu | Claudio Greco | Aliia Erofeeva | Somayeh Jafaritazehjan | Guido Linders | Marc Tanti | Alberto Testoni | Raffaella Bernardi | Albert Gatt
Proceedings of the 27th International Conference on Computational Linguistics

Capturing semantic relations between sentences, such as entailment, is a long-standing challenge for computational semantics. Logic-based models analyse entailment in terms of possible worlds (interpretations, or situations) where a premise P entails a hypothesis H iff in all worlds where P is true, H is also true. Statistical models view this relationship probabilistically, addressing it in terms of whether a human would likely infer H from P. In this paper, we wish to bridge these two perspectives, by arguing for a visually-grounded version of the Textual Entailment task. Specifically, we ask whether models can perform better if, in addition to P and H, there is also an image (corresponding to the relevant “world” or “situation”). We use a multimodal version of the SNLI dataset (Bowman et al., 2015) and we compare “blind” and visually-augmented models of textual entailment. We show that visual information is beneficial, but we also conduct an in-depth error analysis that reveals that current multimodal models are not performing “grounding” in an optimal fashion.


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What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator?
Marc Tanti | Albert Gatt | Kenneth Camilleri
Proceedings of the 10th International Conference on Natural Language Generation

Image captioning has evolved into a core task for Natural Language Generation and has also proved to be an important testbed for deep learning approaches to handling multimodal representations. Most contemporary approaches rely on a combination of a convolutional network to handle image features, and a recurrent network to encode linguistic information. The latter is typically viewed as the primary “generation” component. Beyond this high-level characterisation, a CNN+RNN model supports a variety of architectural designs. The dominant model in the literature is one in which visual features encoded by a CNN are “injected” as part of the linguistic encoding process, driving the RNN’s linguistic choices. By contrast, it is possible to envisage an architecture in which visual and linguistic features are encoded separately, and merged at a subsequent stage. In this paper, we address two related questions: (1) Is direct injection the best way of combining multimodal information, or is a late merging alternative better for the image captioning task? (2) To what extent should a recurrent network be viewed as actually generating, rather than simply encoding, linguistic information?

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LCT-MALTA’s Submission to RepEval 2017 Shared Task
Hoa Trong Vu | Thuong-Hai Pham | Xiaoyu Bai | Marc Tanti | Lonneke van der Plas | Albert Gatt
Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP

System using BiLSTM and max pooling. Embedding is enhanced by POS, character and dependency info.