Distinguishing Fictional Voices: a Study of Authorship Verification Models for Quotation Attribution

Recent approaches to automatically detect the speaker of an utterance of direct speech often disregard general information about characters in favor of local information found in the context, such as surrounding mentions of entities. In this work, we explore stylistic representations of characters built by encoding their quotes with off-the-shelf pretrained Authorship Verification models in a large corpus of English novels (the Project Dialogism Novel Corpus). Results suggest that the combination of stylistic and topical information captured in some of these models accurately distinguish characters among each other, but does not necessarily improve over semantic-only models when attributing quotes. However, these results vary across novels and more investigation of stylometric models particularly tailored for literary texts and the study of characters should be conducted.

Learning representations with end-to-end models for improved remaining useful life prognostics

The remaining Useful Life (RUL) of equipment is defined as the duration between the current time and its failure. An accurate and reliable prognostic of the remaining useful life provides decision-makers with valuable information to adopt an appropriate maintenance strategy to maximize equipment utilization and avoid costly breakdowns. In this work, we propose an end-to-end deep learning model based on multi-layer perceptron and long short-term memory layers (LSTM) to predict the RUL. After normalization of all data, inputs are fed directly to an MLP layers for feature learning, then to an LSTM layer to capture temporal dependencies, and finally to other MLP layers for RUL prognostic. The proposed architecture is tested on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) dataset. Despite its simplicity with respect to other recently proposed models, the model developed outperforms them with a significant decrease in the competition score and in the root mean square error score between the predicted and the gold value of the RUL. In this paper, we will discuss how the proposed end-to-end model is able to achieve such good results and compare it to other deep learning and state-of-the-art methods.

On the Effects of Using word2vec Representations in Neural Networks for Dialogue Act Recognition

Dialogue act recognition is an important component of a large number of natural language processing pipelines. Many research works have been carried out in this area, but relatively few investigate deep neural networks and word embeddings. This is surprising, given that both of these techniques have proven exceptionally good in most other language-related domains. We propose in this work a new deep neural network that explores recurrent models to capture word sequences within sentences, and further study the impact of pretrained word embeddings. We validate this model on three languages: English, French and Czech. The performance of the proposed approach is consistent across these languages and it is comparable to the state-of-the-art results in English. More importantly, we confirm that deep neural networks indeed outperform a Maximum Entropy classifier, which was expected. However , and this is more surprising, we also found that standard word2vec em-beddings do not seem to bring valuable information for this task and the proposed model, whatever the size of the training corpus is. We thus further analyse the resulting embeddings and conclude that a possible explanation may be related to the mismatch between the type of lexical-semantic information captured by the word2vec embeddings, and the kind of relations between words that is the most useful for the dialogue act recognition task.

Cross-lingual Transfer Learning for Dialogue Act Recognition

This paper deals with cross-lingual transfer learning for dialogue act (DA) recognition. Besides generic contextual information gathered from pre-trained BERT embeddings, our objective is to transfer models trained on a standard English DA corpus to two other languages, German and French, and to potentially very different types of dialogue with different dialogue acts than the standard well-known DA corpora. The proposed approach thus studies the applicability of automatic DA recognition to specific tasks that may not benefit from a large enough number of manual annotations. A key component of our architecture is the automatic translation module, which limitations are addressed by stacking both foreign and translated words sequences into the same model. We further compare both CNN and multi-head self-attention to compute the speaker turn embeddings and show that in low-resource situations, the best results are obtained by combining all sources of transferred information.

Quality of syntactic implication of RL-based sentence summarization

Work on summarization has explored both reinforcement learning (RL) optimization using ROUGE as a reward and syntax-aware models, such as models those input is enriched with part-of-speech (POS)-tags and dependency information. However, it is not clear what is the respective impact of these approaches beyond the standard ROUGE evaluation metric. Especially, RL-based for summarization is becoming more and more popular. In this paper, we provide a detailed comparison of these two approaches and of their combination along several dimensions that relate to the perceived quality of the generated summaries: number of repeated words, distribution of part-of-speech tags, impact of sentence length, relevance and grammaticality. Using the standard Gigaword sentence summarization task, we compare an RL self-critical sequence training (SCST) method with syntax-aware models that leverage POS tags and Dependency information. We show that on all qualitative evaluations, the combined model gives the best results, but also that only training with RL and without any syntactic information already gives nearly as good results as syntax-aware models with less parameters and faster training convergence.

Multi-lingual Dialogue Act Recognition with Deep Learning Methods

This paper deals with multi-lingual dialogue act (DA) recognition. The proposed approaches are based on deep neural networks and use word2vec embeddings for word representation. Two multi-lingual models are proposed for this task. The first approach uses one general model trained on the embeddings from all available languages. The second method trains the model on a single pivot language and a linear transformation method is used to project other languages onto the pivot language. The popular convolutional neural network and LSTM architectures with different set-ups are used as classifiers. To the best of our knowledge this is the first attempt at multi-lingual DA recognition using neural networks. The multi-lingual models are validated experimentally on two languages from the Verbmobil corpus.

Multi-task dialog act and sentiment recognition on Mastodon

Because of license restrictions, it often becomes impossible to strictly reproduce most research results on Twitter data already a few months after the creation of the corpus. This situation worsened gradually as time passes and tweets become inaccessible. This is a critical issue for reproducible and accountable research on social media. We partly solve this challenge by annotating a new Twitter-like corpus from an alternative large social medium with licenses that are compatible with reproducible experiments: Mastodon. We manually annotate both dialogues and sentiments on this corpus, and train a multi-task hierarchical recurrent network on joint sentiment and dialog act recognition. We experimentally demonstrate that transfer learning may be efficiently achieved between both tasks, and further analyze some specific correlations between sentiments and dialogues on social media. Both the annotated corpus and deep network are released with an open-source license.

Do Convolutional Networks need to be Deep for Text Classification ?

We study in this work the importance of depth in convolutional models for text classification, either when character or word inputs are considered. We show on 5 standard text classification and sentiment analysis tasks that deep models indeed give better performances than shallow networks when the text input is represented as a sequence of characters. However, a simple shallow-and-wide network outperforms deep models such as DenseNet with word inputs. Our shallow word model further establishes new state-of-the-art performances on two datasets: Yelp Binary (95.9\%) and Yelp Full (64.9\%).