Syntactic manipulation for generating more diverse and interesting texts

Natural Language Generation plays an important role in the domain of dialogue systems as it determines how users perceive the system. Recently, deep-learning based systems have been proposed to tackle this task, as they generalize better and require less amounts of manual effort to implement them for new domains. However, deep learning systems usually adapt a very homogeneous sounding writing style which expresses little variation. In this work, we present our system for Natural Language Generation where we control various aspects of the surface realization in order to increase the lexical variability of the utterances, such that they sound more diverse and interesting. For this, we use a Semantically Controlled Long Short-term Memory Network (SCLSTM), and apply its specialized cell to control various syntactic features of the generated texts. We present an in-depth human evaluation where we show the effects of these surface manipulation on the perception of potential users

Sentiment analysis using convolutional neural networks with multi-task training and distant supervision on italian tweets

In this paper, we propose a classifier for predicting sentiments of Italian Twitter messages. This work builds upon a deep learning approach where we leverage large amounts of weakly labelled data to train a 2-layer convolutional neural network. To train our network we apply a form of multi-task training. Our system participated in the EvalItalia-2016 competition and outperformed all other approaches on the sentiment analysis task

End-to-end trainable system for enhancing diversity in natural language generation

Natural Language Generation plays an important role in the domain of dialogue systems as it determines how the users perceive the system. Recently, deep-learning based systems have been proposed to tackle this task, as they generalize better and do not require large amounts of manual effort to implement them for new domains. However, deep learning systems usually produce monotonous sounding texts. In this work, we present our system for Natural Language Generation where we control the first word of the surface realization. We show that with this simple control mechanism it is possible to increase the lexical variability and the complexity of the generated texts. For this, we apply a character-based version of the Semantically Controlled Long Short-term Memory Network (SC-LSTM), and apply its specialized cell to control the first word generated by the system. To ensure that the surface manipulation does not produce semantically incoherent texts we apply a semantic control component, which we also use for reranking purposes. We show that our model is capable of generating texts that are more sophisticated while decreasing the number of semantic errors made during the generation

Fact-aware abstractive text summarization using a pointer-generator network

German Summarization Challenge 2019 at SwissText 201

SwissAlps at SemEval-2017 Task 3 : attention-based convolutional neural network for community question answering

In this paper we propose a system for re-ranking answers for a given question. Our method builds on a siamese CNN architecture which is extended by two attention mechanisms. The approach was evaluated on the datasets of the SemEval-2017 competition for Community Question Answering (cQA), where it achieved 7th place obtaining a MAP score of 86.24 points on the Question-Comment Similarity subtask

A Twitter corpus and benchmark resources for german sentiment analysis

In this paper we present SB10k, a new corpus for sentiment analysis with approx.10,000 German tweets. We use this new corpus and two existing corpora to provide state-of-the-art bench-marks for sentiment analysis in German:we implemented a CNN (based on the winning system of SemEval-2016) and a feature-based SVM and compare their performance on all three corpora. For the CNN, we also created German word embeddings trained on 300M tweets. These word embeddings were then optimized for sentiment analysis using distant-supervised learning. The new corpus, the German word embeddings (plain and optimized), and source code to re-run the benchmarks are publicly available

TopicThunder at SemEval-2017 Task 4 : sentiment classification using a convolutional neural network with distant supervision

In this paper, we propose a classifier for predicting topic-specific sentiments of English Twitter messages. Our method is based on a 2-layer CNN. With a distant supervised phase we leverage a large amount of weakly-labelled training data. Our system was evaluated on the data provided by the SemEval-2017 competition in the Topic-Based Message Polarity Classification subtask, where it ranked 4th place

Potential and limitations of cross-domain sentiment classification

In this paper we investigate the cross-domain performance of sentiment analysis systems. For this purpose we train a convolutional neural network (CNN) on data from different domains and evaluate its performance on other domains. Furthermore, we evaluate the usefulness of combining a large amount of different smaller annotated corpora to a large corpus. Our results show that more sophisticated approaches are required to train a system that works equally well on various domains

ZHAW-CAI at CheckThat! 2023 : ensembling using kernel averaging

We describe our approaches to sub-task 1A on multi-modal check-worthiness classification of the CheckThat! Lab 2023 in English. The goal was to determine whether a tweet is worth fact-checking based on its text and image content. Our submission was based on a kernel ensemble of different uni-modal and multi-modal classifiers. It achieved second place out of 7 teams with an F1 score of 0.708

Are we summarizing the right way? : a survey of dialogue summarization data sets

Dialogue summarization is a long-standing task in the field of NLP, and several data sets with dialogues and associated human-written summaries of different styles exist. However, it is unclear for which type of dialogue which type of summary is most appropriate. For this reason, we apply a linguistic model of dialogue types to derive matching summary items and NLP tasks. This allows us to map existing dialogue summarization data sets into this model and identify gaps and potential directions for future work. As part of this process, we also provide an extensive overview of existing dialogue summarization data sets