UNIST-Eunchan/NLP-Paper-to-QA-Generation huggingface dataset:
Abstract: In this paper, we propose a novel domain adaptation method named"mixed fine tuning"for neural machine translation (NMT). We combine two existing approaches namely fine tuning and multi domain NMT. We first train an NMT model on an out-of-domain parallel corpus, and then fine tune it on a parallel corpus which is a mix of the in-domain and out-of-domain corpora. All corpora are augmented with artificial tags to indicate specific domains. We empirically compare our proposed method against fine tuning and multi domain methods and discuss its benefits and shortcomings.

Introduction: One of the most attractive features of neural machine translation (NMT) BIBREF0 , BIBREF1 , BIBREF2 is that it is possible to train an end to end system without the need to deal with word alignments, translation rules and complicated decoding algorithms, which are a characteristic of statistical machine translation (SMT) systems. However, it is reported that NMT works better than SMT only when there is an abundance of parallel corpora. In the case of low resource domains, vanilla NMT is either worse than or comparable to SMT BIBREF3 . Domain adaptation has been shown to be effective for low resource NMT. The conventional domain adaptation method is fine tuning, in which an out-of-domain model is further trained on in-domain data BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 . However, fine tuning tends to overfit quickly due to the small size of the in-domain data. On the other hand, multi domain NMT BIBREF8 involves training a single NMT model for multiple domains. This method adds tags “<2domain>" by modifying the parallel corpora to indicate domains without any modifications to the NMT system architecture. However, this method has not been studied for domain adaptation in particular. Motivated by these two lines of studies, we propose a new domain adaptation method called “mixed fine tuning," where we first train an NMT model on an out-of-domain parallel corpus, and then fine tune it on a parallel corpus that is a mix of the in-domain and out-of-domain corpora. Fine tuning on the mixed corpus instead of the in-domain corpus can address the overfitting problem. All corpora are augmented with artificial tags to indicate specific domains. We tried two different corpora settings: We observed that “mixed fine tuning" works significantly better than methods that use fine tuning and domain tag based approaches separately. Our contributions are twofold:

Question: How much improvement does their method get over the fine tuning baseline?
Answer: 0.08 points on the 2011 test set, 0.44 points on the 2012 test set, 0.42 points on the 2013 test set for IWSLT-CE.
Abstract: Recognizing Musical Entities is important for Music Information Retrieval (MIR) since it can improve the performance of several tasks such as music recommendation, genre classification or artist similarity. However, most entity recognition systems in the music domain have concentrated on formal texts (e.g. artists' biographies, encyclopedic articles, etc.), ignoring rich and noisy user-generated content. In this work, we present a novel method to recognize musical entities in Twitter content generated by users following a classical music radio channel. Our approach takes advantage of both formal radio schedule and users' tweets to improve entity recognition. We instantiate several machine learning algorithms to perform entity recognition combining task-specific and corpus-based features. We also show how to improve recognition results by jointly considering formal and user-generated content

Introduction: The increasing use of social media and microblogging services has broken new ground in the field of Information Extraction (IE) from user-generated content (UGC). Understanding the information contained in users' content has become one of the main goal for many applications, due to the uniqueness and the variety of this data BIBREF0 . However, the highly informal and noisy status of these sources makes it difficult to apply techniques proposed by the NLP community for dealing with formal and structured content BIBREF1 . In this work, we analyze a set of tweets related to a specific classical music radio channel, BBC Radio 3, interested in detecting two types of musical named entities, Contributor and Musical Work. The method proposed makes use of the information extracted from the radio schedule for creating links between users' tweets and tracks broadcasted. Thanks to this linking, we aim to detect when users refer to entities included into the schedule. Apart from that, we consider a series of linguistic features, partly taken from the NLP literature and partly specifically designed for this task, for building statistical models able to recognize the musical entities. To that aim, we perform several experiments with a supervised learning model, Support Vector Machine (SVM), and a recurrent neural network architecture, a bidirectional LSTM with a CRF layer (biLSTM-CRF). The contributions in this work are summarized as follows: The paper is structured as follows. In Section 2, we present a review of the previous works related to Named Entity Recognition, focusing on its application on UGC and MIR. Afterwards, in Section 3 it is presented the methodology of this work, describing the dataset and the method proposed. In Section 4, the results obtained are shown. Finally, in Section 5 conclusions are discussed.

Question: What language is the Twitter content in?
Answer: English
Abstract: We present an experimental dataset, Basic Dataset for Sorani Kurdish Automatic Speech Recognition (BD-4SK-ASR), which we used in the first attempt in developing an automatic speech recognition for Sorani Kurdish. The objective of the project was to develop a system that automatically could recognize simple sentences based on the vocabulary which is used in grades one to three of the primary schools in the Kurdistan Region of Iraq. We used CMUSphinx as our experimental environment. We developed a dataset to train the system. The dataset is publicly available for non-commercial use under the CC BY-NC-SA 4.0 license.

Introduction: Kurdish language processing requires endeavor by interested researchers and scholars to overcome with a large gap which it has regarding the resource scarcity. The areas that need attention and the efforts required have been addressed in BIBREF0. The Kurdish speech recognition is an area which has not been studied so far. We were not able to retrieve any resources in the literature regarding this subject. In this paper, we present a dataset based on CMUShpinx BIBREF1 for Sorani Kurdish. We call it a Dataset for Sorani Kurdish Automatic Speech Recognition (BD-4SK-ASR). Although other technologies are emerging, CMUShpinx could still be used for experimental studies. The rest of this paper is organized as follows. Section SECREF2 reviews the related work. Section SECREF3 presents different parts of the dataset, such as the dictionary, phoneset, transcriptions, corpus, and language model. Finally, Section SECREF4 concludes the paper and suggests some areas for future work.

Question: How was the dataset collected?
Answer: extracted text from Sorani Kurdish books of primary school and randomly created sentences
Abstract: Information Extraction (IE) refers to automatically extracting structured relation tuples from unstructured texts. Common IE solutions, including Relation Extraction (RE) and open IE systems, can hardly handle cross-sentence tuples, and are severely restricted by limited relation types as well as informal relation specifications (e.g., free-text based relation tuples). In order to overcome these weaknesses, we propose a novel IE framework named QA4IE, which leverages the flexible question answering (QA) approaches to produce high quality relation triples across sentences. Based on the framework, we develop a large IE benchmark with high quality human evaluation. This benchmark contains 293K documents, 2M golden relation triples, and 636 relation types. We compare our system with some IE baselines on our benchmark and the results show that our system achieves great improvements.

Introduction: Information Extraction (IE), which refers to extracting structured information (i.e., relation tuples) from unstructured text, is the key problem in making use of large-scale texts. High quality extracted relation tuples can be used in various downstream applications such as Knowledge Base Population BIBREF0 , Knowledge Graph Acquisition BIBREF1 , and Natural Language Understanding. However, existing IE systems still cannot produce high-quality relation tuples to effectively support downstream applications.

Question: What QA models were used?
Answer: A pointer network decodes the answer from a bidirectional LSTM with attention flow layer and self-matching layer, whose inputs come from word and character embeddings of the query and input text fed through a highway layer.
Abstract: Commonsense Reading Comprehension (CRC) is a significantly challenging task, aiming at choosing the right answer for the question referring to a narrative passage, which may require commonsense knowledge inference. Most of the existing approaches only fuse the interaction information of choice, passage, and question in a simple combination manner from a \emph{union} perspective, which lacks the comparison information on a deeper level. Instead, we propose a Multi-Perspective Fusion Network (MPFN), extending the single fusion method with multiple perspectives by introducing the \emph{difference} and \emph{similarity} fusion\deleted{along with the \emph{union}}. More comprehensive and accurate information can be captured through the three types of fusion. We design several groups of experiments on MCScript dataset \cite{Ostermann:LREC18:MCScript} to evaluate the effectiveness of the three types of fusion respectively. From the experimental results, we can conclude that the difference fusion is comparable with union fusion, and the similarity fusion needs to be activated by the union fusion. The experimental result also shows that our MPFN model achieves the state-of-the-art with an accuracy of 83.52\% on the official test set.

Introduction: Content: Task Definition 1. Describe the task of commonsense reading comprehension(CRC) belongs to which filed and how important it is. 2. Define the task of CRC 3. Data feature of CRC 4. Figure 1 shows an example. Machine Reading Comprehension (MRC) is an extremely challenging topic in natural language processing field. It requires a system to answer the question referring to a given passage.no matter whether the answer is mentioned in the passage. MRC consists of several sub-tasks, such as cloze-style reading comprehension, span-extraction reading comprehension, and open-domain reading comprehension. Most of existing datasets emphasize the question whose answer is mentioned in the passage since it does not need any commonsense. In real reading comprehension, the human reader can fully understand the passage with the prior knowledge to answer the question. To directly relate commonsense knowledge to reading comprehension, SemEval2018 Task 11 defines a new sub-task called Commonsense Reading Comprehension, aiming at answering the questions that requires both commonsense knowledge and the understanding of the passage. The challenge of this task is how tolies in answer questions requires a system to draw inferences from multiple sentences from the passage and requireswith the commonsense knowledge that does not appear in the passage explicitly. Table 1 shows an example of CRC.

Question: What baseline models do they compare against?
Answer: SLQA, Rusalka, HMA Model (single), TriAN (single), jiangnan (ensemble), MITRE (ensemble), TriAN (ensemble), HMA Model (ensemble)
Abstract: It is generally believed that direct sequence-to-sequence (seq2seq) speech recognition models are competitive with hybrid models only when a large amount of data, at least a thousand hours, is available for training. In this paper, we show that state-of-the-art recognition performance can be achieved on the Switchboard-300 database using a single headed attention, LSTM based model. Using a cross-utterance language model, our single-pass speaker independent system reaches 6.4% and 12.5% word error rate (WER) on the Switchboard and CallHome subsets of Hub5'00, without a pronunciation lexicon. While careful regularization and data augmentation are crucial in achieving this level of performance, experiments on Switchboard-2000 show that nothing is more useful than more data.

Introduction: Powerful neural networks have enabled the use of “end-to-end” speech recognition models that directly map a sequence of acoustic features to a sequence of words without conditional independence assumptions. Typical examples are attention based encoder-decoder BIBREF0 and recurrent neural network transducer models BIBREF1. Due to training on full sequences, an utterance corresponds to a single observation from the view point of these models; thus, data sparsity is a general challenge for such approaches, and it is believed that these models are effective only when sufficient training data is available. Indeed, many end-to-end speech recognition papers focus on LibriSpeech, which has 960 hours of training audio. Nevertheless, the best performing systems follow the traditional hybrid approach BIBREF2, outperforming attention based encoder-decoder models BIBREF3, BIBREF4, BIBREF5, BIBREF6, and when less training data is used, the gap between “end-to-end” and hybrid models is more prominent BIBREF3, BIBREF7. Several methods have been proposed to tackle data sparsity and overfitting problems; a detailed list can be found in Sec. SECREF2. Recently, increasingly complex attention mechanisms have been proposed to improve seq2seq model performance, including stacking self and regular attention layers and using multiple attention heads in the encoder and decoder BIBREF4, BIBREF8. We show that consistent application of various regularization techniques brings a simple, single-head LSTM attention based encoder-decoder model to state-of-the-art performance on Switchboard-300, a task where data sparsity is more severe than LibriSpeech. We also note that remarkable performance has been achieved with single-head LSTM models in a recent study on language modeling BIBREF9.

Question: How much bigger is Switchboard-2000 than Switchboard-300 database?
Answer: Switchboard-2000 contains 1700 more hours of speech data.
Abstract: The use of irony and sarcasm in social media allows us to study them at scale for the first time. However, their diversity has made it difficult to construct a high-quality corpus of sarcasm in dialogue. Here, we describe the process of creating a large- scale, highly-diverse corpus of online debate forums dialogue, and our novel methods for operationalizing classes of sarcasm in the form of rhetorical questions and hyperbole. We show that we can use lexico-syntactic cues to reliably retrieve sarcastic utterances with high accuracy. To demonstrate the properties and quality of our corpus, we conduct supervised learning experiments with simple features, and show that we achieve both higher precision and F than previous work on sarcasm in debate forums dialogue. We apply a weakly-supervised linguistic pattern learner and qualitatively analyze the linguistic differences in each class.

Introduction: Irony and sarcasm in dialogue constitute a highly creative use of language signaled by a large range of situational, semantic, pragmatic and lexical cues. Previous work draws attention to the use of both hyperbole and rhetorical questions in conversation as distinct types of lexico-syntactic cues defining diverse classes of sarcasm BIBREF0 . Theoretical models posit that a single semantic basis underlies sarcasm's diversity of form, namely "a contrast" between expected and experienced events, giving rise to a contrast between what is said and a literal description of the actual situation BIBREF1 , BIBREF2 . This semantic characterization has not been straightforward to operationalize computationally for sarcasm in dialogue. Riloffetal13 operationalize this notion for sarcasm in tweets, achieving good results. Joshietal15 develop several incongruity features to capture it, but although they improve performance on tweets, their features do not yield improvements for dialogue. Previous work on the Internet Argument Corpus (IAC) 1.0 dataset aimed to develop a high-precision classifier for sarcasm in order to bootstrap a much larger corpus BIBREF3 , but was only able to obtain a precision of just 0.62, with a best F of 0.57, not high enough for bootstrapping BIBREF4 , BIBREF5 . Justoetal14 experimented with the same corpus, using supervised learning, and achieved a best precision of 0.66 and a best F of 0.70. Joshietal15's explicit congruity features achieve precision around 0.70 and best F of 0.64 on a subset of IAC 1.0. We decided that we need a larger and more diverse corpus of sarcasm in dialogue. It is difficult to efficiently gather sarcastic data, because only about 12% of the utterances in written online debate forums dialogue are sarcastic BIBREF6 , and it is difficult to achieve high reliability for sarcasm annotation BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 . Thus, our contributions are:

Question: What are the linguistic differences between each class?
Answer: Each class has different patterns in adjectives, adverbs and verbs for sarcastic and non-sarcastic classes
Abstract: Reducing hateful and offensive content in online social media pose a dual problem for the moderators. On the one hand, rigid censorship on social media cannot be imposed. On the other, the free flow of such content cannot be allowed. Hence, we require efficient abusive language detection system to detect such harmful content in social media. In this paper, we present our machine learning model, HateMonitor, developed for Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC), a shared task at FIRE 2019. We have used a Gradient Boosting model, along with BERT and LASER embeddings, to make the system language agnostic. Our model came at First position for the German sub-task A. We have also made our model public at this https URL .

Introduction: In social media, abusive language denotes a text which contains any form of unacceptable language in a post or a comment. Abusive language can be divided into hate speech, offensive language and profanity. Hate speech is a derogatory comment that hurts an entire group in terms of ethnicity, race or gender. Offensive language is similar to derogatory comment, but it is targeted towards an individual. Profanity refers to any use of unacceptable language without a specific target. While profanity is the least threatening, hate speech has the most detrimental effect on the society. Social media moderators are having a hard time in combating the rampant spread of hate speech as it is closely related to the other forms of abusive language. The evolution of new slangs and multilingualism, further adding to the complexity. Recently, there has been a sharp rise in hate speech related incidents in India, the lynchings being the clear indication BIBREF1. Arun et al. BIBREF1 suggests that hate speech in India is very complicated as people are not directly spreading hate but are spreading misinformation against a particular community. Hence, it has become imperative to study hate speech in Indian language. For the first time, a shared task on abusive content detection has been released for Hindi language at HASOC 2019. This will fuel the hate speech and offensive language research for Indian languages. The inclusion of datasets for English and German language will give a performance comparison for detection of abusive content in high and low resource language. In this paper, we focus on the detection of multilingual hate speech detection that are written in Hindi, English, and German and describe our submission (HateMonitors) for HASOC at FIRE 2019 competition. Our system concatenates two types of sentence embeddings to represent each tweet and use machine learning models for classification.

Question: What are the languages used to test the model?
Answer: Hindi, English and German (German task won)
Abstract: This paper proposes a method for classifying movie genres by only looking at text reviews. The data used are from Large Movie Review Dataset v1.0 and IMDb. This paper compared a K-nearest neighbors (KNN) model and a multilayer perceptron (MLP) that uses tf-idf as input features. The paper also discusses different evaluation metrics used when doing multi-label classification. For the data used in this research, the KNN model performed the best with an accuracy of 55.4\% and a Hamming loss of 0.047.

Introduction: By only reading a single text review of a movie it can be difficult to say what the genre of that movie is, but by using text mining techniques on thousands of movie reviews is it possible to predict the genre? This paper explores the possibility of classifying genres of a movie based only on a text review of that movie. This is an interesting problem because to the naked eye it may seem difficult to predict the genre by only looking at a text review. One example of a review can be seen in the following example: I liked the film. Some of the action scenes were very interesting, tense and well done. I especially liked the opening scene which had a semi truck in it. A very tense action scene that seemed well done. Some of the transitional scenes were filmed in interesting ways such as time lapse photography, unusual colors, or interesting angles. Also the film is funny is several parts. I also liked how the evil guy was portrayed too. I'd give the film an 8 out of 10. http://www.imdb.com/title/tt0211938/reviews From the quoted review, one could probably predict the movie falls in the action genre; however, it would be difficult to predict all three of the genres (action, comedy, crime) that International Movie Database (IMDB) lists. With the use of text mining techniques it is feasible to predict multiple genres based on a review. There are numerous previous works on classifying the sentiment of reviews, e.g., maas-EtAl:2011:ACL-HLT2011 by BIBREF0 . There are fewer scientific papers available on specifically classifying movie genres based on reviews; therefore, inspiration for this paper comes from papers describing classification of text for other or general contexts. One of those papers is DBLP:journals/corr/cmp-lg-9707002 where BIBREF1 describe how to use a multilayer perceptron (MLP) for genre classification. All data, in the form of reviews and genres, used in this paper originates from IMDb.

Question: what was the baseline?
Answer: There is no baseline.