Predicting stock prices remains a significant challenge in financial markets. This study explores existing stock price prediction systems, identifies their strengths and weaknesses, and proposes a novel method for stock price prediction that leverages a state-of-the-art neural network framework, combining the BERT language model for sentiment analysis on news articles and the GAN model for stock price prediction. We introduce the FB-GAN model, an ensemble model that leverages stock price history and market sentiment score for more accurate stock price prediction and propose effective strategies to capture the market sentiment. We conduct experiments on stock price prediction for five major equities (Amazon, Apple, Microsoft, Nvidia, and Adobe), and compare the performance obtained by our proposed model against the existing state-of-the-art baseline model. The results demonstrate that our proposed model outperforms existing models across the five major equities. We demonstrate that the strategic incorporation of market sentiment using both headlines as well summaries of news articles significantly enhances the accuracy and robustness of stock price prediction.

A salient feature of Neural Machine Translation (NMT) is the end-to-end nature of training employed, eschewing the need of separate components to model different linguistic phenomena. Rather, an NMT model learns to translate individual sentences from the labeled data itself. However, traditional NMT methods trained on large parallel corpora with a one-to-one sentence mapping make an implicit assumption of sentence independence. This makes it challenging for current NMT systems to model inter-sentential discourse phenomena. While recent research in this direction mainly leverages a single previous source sentence to model discourse, this paper proposes the incorporation of a context window spanning previous as well as next sentences as source-side context and previously generated output as target-side context, using an effective non-recurrent architecture based on self-attention. Experiments show improvement over non-contextual models as well as contextual methods using only previous context.

Recent approaches to the Automatic Post-editing (APE) of Machine Translation (MT) have shown that best results are obtained by neural multi-source models that correct the raw MT output by also considering information from the corresponding source sentence. To this aim, we present for the first time a neural multi-source APE model based on the Transformer architecture. Moreover, we employ sequence-level loss functions in order to avoid exposure bias during training and to be consistent with the automatic evaluation metrics used for the task. These are the main features of our submissions to the WMT 2018 APE shared task, where we participated both in the PBSMT subtask (i.e. the correction of MT outputs from a phrase-based system) and in the NMT subtask (i.e. the correction of neural outputs). In the first subtask, our system improves over the baseline up to -5.3 TER and +8.23 BLEU points ranking second out of 11 submitted runs. In the second one, characterized by the higher quality of the initial translations, we report lower but statistically significant gains (up to -0.38 TER and +0.8 BLEU), ranking first out of 10 submissions.