UNIST-Eunchan/NLP-Paper-to-QA-Generation huggingface dataset:
Abstract: The recently proposed Sequence-to-Sequence (seq2seq) framework advocates replacing complex data processing pipelines, such as an entire automatic speech recognition system, with a single neural network trained in an end-to-end fashion. In this contribution, we analyse an attention-based seq2seq speech recognition system that directly transcribes recordings into characters. We observe two shortcomings: overconfidence in its predictions and a tendency to produce incomplete transcriptions when language models are used. We propose practical solutions to both problems achieving competitive speaker independent word error rates on the Wall Street Journal dataset: without separate language models we reach 10.6% WER, while together with a trigram language model, we reach 6.7% WER.

Introduction: Deep learning BIBREF0 has led to many breakthroughs including speech and image recognition BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 . A subfamily of deep models, the Sequence-to-Sequence (seq2seq) neural networks have proved to be very successful on complex transduction tasks, such as machine translation BIBREF7 , BIBREF8 , BIBREF9 , speech recognition BIBREF10 , BIBREF11 , BIBREF12 , and lip-reading BIBREF13 . Seq2seq networks can typically be decomposed into modules that implement stages of a data processing pipeline: an encoding module that transforms its inputs into a hidden representation, a decoding (spelling) module which emits target sequences and an attention module that computes a soft alignment between the hidden representation and the targets. Training directly maximizes the probability of observing desired outputs conditioned on the inputs. This discriminative training mode is fundamentally different from the generative "noisy channel" formulation used to build classical state-of-the art speech recognition systems. As such, it has benefits and limitations that are different from classical ASR systems. Understanding and preventing limitations specific to seq2seq models is crucial for their successful development. Discriminative training allows seq2seq models to focus on the most informative features. However, it also increases the risk of overfitting to those few distinguishing characteristics. We have observed that seq2seq models often yield very sharp predictions, and only a few hypotheses need to be considered to find the most likely transcription of a given utterance. However, high confidence reduces the diversity of transcripts obtained using beam search. During typical training the models are conditioned on ground truth transcripts and are scored on one-step ahead predictions. By itself, this training criterion does not ensure that all relevant fragments of the input utterance are transcribed. Subsequently, mistakes that are introduced during decoding may cause the model to skip some words and jump to another place in the recording. The problem of incomplete transcripts is especially apparent when external language models are used.

Question: What are the solutions proposed for the seq2seq shortcomings?
Answer: label smoothing, use of coverage
Abstract: Nigerian Pidgin is arguably the most widely spoken language in Nigeria. Variants of this language are also spoken across West and Central Africa, making it a very important language. This work aims to establish supervised and unsupervised neural machine translation (NMT) baselines between English and Nigerian Pidgin. We implement and compare NMT models with different tokenization methods, creating a solid foundation for future works.

Introduction: Over 500 languages are spoken in Nigeria, but Nigerian Pidgin is the uniting language in the country. Between three and five million people are estimated to use this language as a first language in performing their daily activities. Nigerian Pidgin is also considered a second language to up to 75 million people in Nigeria, accounting for about half of the country's population according to BIBREF0. The language is considered an informal lingua franca and offers several benefits to the country. In 2020, 65% of Nigeria's population is estimated to have access to the internet according to BIBREF1. However, over 58.4% of the internet's content is in English language, while Nigerian languages, such as Igbo, Yoruba and Hausa, account for less than 0.1% of internet content according to BIBREF2. For Nigerians to truly harness the advantages the internet offers, it is imperative that English content is able to be translated to Nigerian languages, and vice versa. This work is a first attempt towards using contemporary neural machine translation (NMT) techniques to perform machine translation for Nigerian Pidgin, establishing solid baselines that will ease and spur future work. We evaluate the performance of supervised and unsupervised neural machine translation models using word-level and the subword-level tokenization of BIBREF3.

Question: How long is their dataset?
Answer: 21214
Abstract: In this paper, we present a real-world conversational AI system to search for and book hotels through text messaging. Our architecture consists of a frame-based dialogue management system, which calls machine learning models for intent classification, named entity recognition, and information retrieval subtasks. Our chatbot has been deployed on a commercial scale, handling tens of thousands of hotel searches every day. We describe the various opportunities and challenges of developing a chatbot in the travel industry.

Introduction: Task-oriented chatbots have recently been applied to many areas in e-commerce. In this paper, we describe a task-oriented chatbot system that provides hotel recommendations and deals. Users access the chatbot through third-party messaging platforms, such as Facebook Messenger (Figure FIGREF4), Amazon Alexa, and WhatsApp. The chatbot elicits information, such as travel dates and hotel preferences, through a conversation, then recommends a set of suitable hotels that the user can then book. Our system uses a dialogue manager that integrates a combination of NLP models to handle the most frequent scenarios, and defer to a human support agent for more difficult situations. The travel industry is an excellent target for e-commerce chatbots for several reasons: Typical online travel agencies provide a web interface (such as buttons, dropdowns, and checkboxes) to enter information and filter search results; this can be difficult to navigate. In contrast, chatbot have a much gentler learning curve, since users interact with the bot using natural language. Additionally, chatbots are lightweight as they are embedded in an instant messaging platform that handles authentication. All of these factors contribute to higher user convenience BIBREF0. Many people book vacations using travel agents, so the idea of booking travel through conversation is already familiar. Thus, we emulate the role of a travel agent, who talks to the customer while performing searches on various supplier databases on his behalf. Our chatbot has the advantage of a narrow focus, so that every conversation is related to booking a hotel. This constrains conversations to a limited set of situations, thus allowing us to develop specialized models to handle hotel-related queries with very high accuracy. The automated component of the chatbot is also closely integrated with human support agents: when the NLP system is unable to understand a customer's intentions, customer support agents are notified and take over the conversation. The agents' feedback is then used to improve the AI, providing valuable training data (Figure FIGREF5). In this paper, we describe our conversational AI systems, datasets, and models.

Question: How is their NER model trained?
Answer: Trained using SpaCy and fine-tuned with their data of hotel and location entities
Abstract: We analyze the performance of different sentiment classification models on syntactically complex inputs like A-but-B sentences. The first contribution of this analysis addresses reproducible research: to meaningfully compare different models, their accuracies must be averaged over far more random seeds than what has traditionally been reported. With proper averaging in place, we notice that the distillation model described in arXiv:1603.06318v4 [cs.LG], which incorporates explicit logic rules for sentiment classification, is ineffective. In contrast, using contextualized ELMo embeddings (arXiv:1802.05365v2 [cs.CL]) instead of logic rules yields significantly better performance. Additionally, we provide analysis and visualizations that demonstrate ELMo's ability to implicitly learn logic rules. Finally, a crowdsourced analysis reveals how ELMo outperforms baseline models even on sentences with ambiguous sentiment labels.

Introduction: In this paper, we explore the effectiveness of methods designed to improve sentiment classification (positive vs. negative) of sentences that contain complex syntactic structures. While simple bag-of-words or lexicon-based methods BIBREF1 , BIBREF2 , BIBREF3 achieve good performance on this task, they are unequipped to deal with syntactic structures that affect sentiment, such as contrastive conjunctions (i.e., sentences of the form “A-but-B”) or negations. Neural models that explicitly encode word order BIBREF4 , syntax BIBREF5 , BIBREF6 and semantic features BIBREF7 have been proposed with the aim of improving performance on these more complicated sentences. Recently, hu2016harnessing incorporate logical rules into a neural model and show that these rules increase the model's accuracy on sentences containing contrastive conjunctions, while PetersELMo2018 demonstrate increased overall accuracy on sentiment analysis by initializing a model with representations from a language model trained on millions of sentences. In this work, we carry out an in-depth study of the effectiveness of the techniques in hu2016harnessing and PetersELMo2018 for sentiment classification of complex sentences. Part of our contribution is to identify an important gap in the methodology used in hu2016harnessing for performance measurement, which is addressed by averaging the experiments over several executions. With the averaging in place, we obtain three key findings: (1) the improvements in hu2016harnessing can almost entirely be attributed to just one of their two proposed mechanisms and are also less pronounced than previously reported; (2) contextualized word embeddings BIBREF0 incorporate the “A-but-B” rules more effectively without explicitly programming for them; and (3) an analysis using crowdsourcing reveals a bigger picture where the errors in the automated systems have a striking correlation with the inherent sentiment-ambiguity in the data.

Question: What logic rules can be learned using ELMo?
Answer: 1).But   2).Eng  3). A-But-B
Abstract: In recent years, online social networks have allowed worldwide users to meet and discuss. As guarantors of these communities, the administrators of these platforms must prevent users from adopting inappropriate behaviors. This verification task, mainly done by humans, is more and more difficult due to the ever growing amount of messages to check. Methods have been proposed to automatize this moderation process, mainly by providing approaches based on the textual content of the exchanged messages. Recent work has also shown that characteristics derived from the structure of conversations, in the form of conversational graphs, can help detecting these abusive messages. In this paper, we propose to take advantage of both sources of information by proposing fusion methods integrating content-and graph-based features. Our experiments on raw chat logs show that the content of the messages, but also of their dynamics within a conversation contain partially complementary information, allowing performance improvements on an abusive message classification task with a final F-measure of 93.26%.

Introduction: In recent years, online social networks have allowed world-wide users to meet and discuss. As guarantors of these communities, the administrators of these platforms must prevent users from adopting inappropriate behaviors. This verification task, mainly done by humans, is more and more difficult due to the ever growing amount of messages to check. Methods have been proposed to automatize this moderation process, mainly by providing approaches based on the textual content of the exchanged messages. Recent work has also shown that characteristics derived from the structure of conversations, in the form of conversational graphs, can help detecting these abusive messages. In this paper, we propose to take advantage of both sources of information by proposing fusion methods integrating content- and graph-based features. Our experiments on raw chat logs show that the content of the messages, but also of their dynamics within a conversation contain partially complementary information, allowing performance improvements on an abusive message classification task with a final INLINEFORM0 -measure of 93.26%.

Question: What graph-based features are considered?
Answer: Top graph based features are: Coreness Score, PageRank Centrality, Strength Centrality, Vertex Count, Closeness Centrality, Closeness Centrality, Authority Score, Hub Score, Reciprocity and Closeness Centrality.
Abstract: With the exponential growth in the usage of social media to share live updates about life, taking pictures has become an unavoidable phenomenon. Individuals unknowingly create a unique knowledge base with these images. The food images, in particular, are of interest as they contain a plethora of information. From the image metadata and using computer vision tools, we can extract distinct insights for each user to build a personal profile. Using the underlying connection between cuisines and their inherent tastes, we attempt to develop such a profile for an individual based solely on the images of his food. Our study provides insights about an individual's inclination towards particular cuisines. Interpreting these insights can lead to the development of a more precise recommendation system. Such a system would avoid the generic approach in favor of a personalized recommendation system.

Introduction: A picture is worth a thousand words. Complex ideas can easily be depicted via an image. An image is a mine of data in the 21st century. With each person taking an average of 20 photographs every day, the number of photographs taken around the world each year is astounding. According to a Statista report on Photographs, an estimated 1.2 trillion photographs were taken in 2017 and 85% of those images were of food. Youngsters can't resist taking drool-worthy pictures of their food before tucking in. Food and photography have been amalgamated into a creative art form where even the humble home cooked meal must be captured in the perfect lighting and in the right angle before digging in. According to a YouGov poll, half of Americans take pictures of their food. The sophistication of smart-phone cameras allows users to capture high quality images on their hand held device. Paired with the increasing popularity of social media platforms such as Facebook and Instagram, it makes sharing of photographs much easier than with the use of a standalone camera. Thus, each individual knowingly or unknowingly creates a food log. A number of applications such as MyFitnessPal, help keep track of a user's food consumption. These applications are heavily dependent on user input after every meal or snack. They often include several data fields that have to be manually filled by the user. This tedious process discourages most users, resulting in a sparse record of their food intake over time. Eventually, this data is not usable. On the other hand, taking a picture of your meal or snack is an effortless exercise. Food images may not give us an insight into the quantity or quality of food consumed by the individual but it can tell us what he/she prefers to eat or likes to eat. We try to tackle the following research question with our work: Can we predict the cuisine of a food item based on just it's picture, with no additional text input from the user?

Question: Is this done in form of unsupervised (clustering) or suppervised learning?
Answer: Unsupervised