z-uo/qasper-squad huggingface dataset:
Paper: Open Event Extraction from Online Text using a Generative Adversarial Network
Recently there has been much interest in event extraction from online texts, and approaches could be categorized as domain-specific and open-domain event extraction.
Question: What datasets are used?
Paper: Open Event Extraction from Online Text using a Generative Adversarial Network
As a neural-based generative model, Generative Adversarial Nets BIBREF3 have been extensively researched in natural language processing (NLP) community.
Question: What alternative to Gibbs sampling is used?
Paper: Open Event Extraction from Online Text using a Generative Adversarial Network
We describe Adversarial-neural Event Model (AEM) in this section. An event is represented as a quadruple < INLINEFORM0 >, where INLINEFORM1 stands for non-location named entities, INLINEFORM2 for a location, INLINEFORM3 for event-related keywords, INLINEFORM4 for a date, and each component in the tuple is represented by component-specific representative words.
Question: How does this model overcome the assumption that all words in a document are generated from a single event?
Answer: by learning a projection function between the document-event distribution and four event related word distributions
Paper: Predicting the Industry of Users on Social Media
Over the past two decades, the emergence of social media has enabled the proliferation of traceable human behavior. The content posted by users can reflect who their friends are, what topics they are interested in, or which company they are working for. At the same time, users are listing a number of profile fields to define themselves to others. The utilization of such metadata has proven important in facilitating further developments of applications in advertising BIBREF0 , personalization BIBREF1 , and recommender systems BIBREF2 . However, profile information can be limited, depending on the platform, or it is often deliberately omitted BIBREF3 . To uncloak this information, a number of studies have utilized social media users' footprints to approximate their profiles.
Question: How many users do they look at?
Paper: Predicting the Industry of Users on Social Media
Alongside the wide adoption of social media by the public, researchers have been leveraging the newly available data to create and refine models of users' behavior and profiling. There exists a myriad research that analyzes language in order to profile social media users. Some studies sought to characterize users' personality BIBREF9 , BIBREF10 , while others sequenced the expressed emotions BIBREF11 , studied mental disorders BIBREF12 , and the progression of health conditions BIBREF13 . At the same time, a number of researchers sought to predict the social media users' age and/or gender BIBREF14 , BIBREF15 , BIBREF16 , while others targeted and analyzed the ethnicity, nationality, and race of the users BIBREF17 , BIBREF18 , BIBREF19 . One of the profile fields that has drawn a great deal of attention is the location of a user. Among others, Hecht et al. Hecht11 predicted Twitter users' locations using machine learning on nationwide and state levels. Later, Han et al. Han14 identified location indicative words to predict the location of Twitter users down to the city level.
Question: What do they mean by a person's industry?
Paper: Predicting the Industry of Users on Social Media
We compile our industry-annotated dataset by identifying blogger profiles located in the U.S. on the profile finder on http://www.blogger.com, and scraping only those users that had the industry profile element completed.
Question: What model did they use for their system?
Paper: Predicting the Industry of Users on Social Media
After collecting our dataset, we split it into three sets: a train set, a development set, and a test set. The sizes of these sets are 17,880, 2,500, and 2,500 users, respectively, with users randomly assigned to these sets. In all the experiments that follow, we evaluate our classifiers by training them on the train set, configure the parameters and measure performance on the development set, and finally report the prediction accuracy and results on the test set. Note that all the experiments are performed at user level, i.e., all the data for one user is compiled into one instance in our data sets.
Question: What social media platform did they look at?
Paper: Predicting the Industry of Users on Social Media
In this section, we seek the effectiveness of using solely textual features obtained from the users' postings to predict their industry.
Question: What are the industry classes defined in this paper?
Answer: technology, religion, fashion, publishing, sports or recreation, real estate, agriculture/environment, law, security/military, tourism, construction, museums or libraries, banking/investment banking, automotive
Technology, Religion, Fashion, Publishing, Sports coach, Real Estate, Law, Environment, Tourism, Construction, Museums, Banking, Security, Automotive.
Paper: Emotion Detection in Text: Focusing on Latent Representation
There have been many advances in machine learning methods which help machines understand human behavior better than ever. One of the most important aspects of human behavior is emotion. If machines could detect human emotional expressions, it could be used to improve on verity of applications such as marketing BIBREF0 , human-computer interactions BIBREF1 , political science BIBREF2 etc.
Question: Do they report results only on English data?
Paper: Emotion Detection in Text: Focusing on Latent Representation
A lot of work has been done on detecting emotion in speech or visual data BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 . But detecting emotions in textual data is a relatively new area that demands more research. There have been many attempts to detect emotions in text using conventional machine learning techniques and handcrafted features in which given the dataset, the authors try to find the best feature set that represents the most and the best information about the text, then passing the converted text as feature vectors to the classifier for training BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19 , BIBREF20 , BIBREF21 , BIBREF22 , BIBREF23 , BIBREF24 , BIBREF25 , BIBREF26 . During the process of creating the feature set, in these methods, some of the most important information in the text such as the sequential nature of the data, and the context will be lost.
Question: What are the hyperparameters of the bi-GRU?
Answer: They use the embedding layer with a size 35 and embedding dimension of 300. They use a dense layer with 70 units and a dropout layer with a rate of 50%.
Paper: Emotion Detection in Text: Focusing on Latent Representation
We compare our approach to two other, the first one uses almost the same tweet data as we use for training, and the second one is the CrowdFlower dataset annotated for emotions.
Question: What baseline is used?
Paper: Emotion Detection in Text: Focusing on Latent Representation
There are not many free datasets available for emotion classification. Most datasets are subject-specific (i.e. news headlines, fairy tails, etc.) and not big enough to train deep neural networks. Here we use the tweet dataset created by Wang et al. As mentioned in the previous section, they have collected over 2 million tweets by using hashtags for labeling their data. They created a list of words associated with 7 emotions (six emotions from BIBREF34 love, joy, surprise, anger, sadness fear plus thankfulness (See Table TABREF3 ), and used the list as their guide to label the sampled tweets with acceptable quality.
Question: What data is used in experiments?
Paper: Emotion Detection in Text: Focusing on Latent Representation
In this section, we introduce the deep neural network architecture that we used to classify emotions in the tweets dataset. Emotional expressions are more complex and context-dependent even compared to other forms of expressions based mostly on the complexity and ambiguity of human emotions and emotional expressions and the huge impact of context on the understanding of the expressed emotion. These complexities are what led us to believe lexicon-based features like is normally used in conventional machine learning approaches are unable to capture the intricacy of emotional expressions.
Question: What meaningful information does the GRU model capture, which traditional ML models do not?
Paper: Short Text Language Identification for Under Resourced Languages
Accurate language identification (LID) is the first step in many natural language processing and machine comprehension pipelines. If the language of a piece of text is known then the appropriate downstream models like parts of speech taggers and language models can be applied as required.
Question: What is the approach of previous work?
Paper: Short Text Language Identification for Under Resourced Languages
The focus of this section is on recently published datasets and LID research applicable to the South African context. An in depth survey of algorithms, features, datasets, shared tasks and evaluation methods may be found in BIBREF0.
Question: Is the lexicon the same for all languages?
Paper: Short Text Language Identification for Under Resourced Languages
The proposed LID algorithm builds on the work in BIBREF8 and BIBREF26. We apply a naive Bayesian classifier with character (2, 4 & 6)-grams, word unigram and word bigram features with a hierarchical lexicon based classifier.
Question: How do they obtain the lexicon?
Paper: Short Text Language Identification for Under Resourced Languages
The average classification accuracy results are summarised in Table TABREF9. The accuracies reported are for classifying a piece of text by its specific language label. Classifying text only by language group or family is a much easier task as reported in BIBREF8.
Question: What evaluation metric is used?
Paper: Short Text Language Identification for Under Resourced Languages
LID of short texts, informal styles and similar languages remains a difficult problem which is actively being researched. The proposed algorithm was evaluated on three existing datasets and compared to the implementations of three public LID implementations as well as to reported results of four other algorithms. It performed well relative to the other methods beating their results. However, the performance is dependent on the support of the lexicon.
Question: Which languages are similar to each other?
Paper: Robustly Leveraging Prior Knowledge in Text Classification
We posses a wealth of prior knowledge about many natural language processing tasks. For example, in text categorization, we know that words such as NBA, player, and basketball are strong indicators of the sports category BIBREF0 , and words like terrible, boring, and messing indicate a negative polarity while words like perfect, exciting, and moving suggest a positive polarity in sentiment classification.
Question: What background knowledge do they leverage?
Answer: labelled features, which are words whose presence strongly indicates a specific class or topic
Paper: Robustly Leveraging Prior Knowledge in Text Classification
We address the robustness problem on top of GE-FL BIBREF0 , a GE method which leverages labeled features as prior knowledge. A labeled feature is a strong indicator of a specific class and is manually provided to the classifier. For example, words like amazing, exciting can be labeled features for class positive in sentiment classification.
Question: What are the three regularization terms?
Paper: Robustly Leveraging Prior Knowledge in Text Classification
Generalized expectation (GE) criteria BIBREF7 provides us a natural way to directly constrain the model in the preferred direction. For example, when we know the proportion of each class of the dataset in a classification task, we can guide the model to predict out a pre-specified class distribution.
Question: What NLP tasks do they consider?
Answer: text classification for themes including sentiment, web-page, science, medical and healthcare
Paper: Robustly Leveraging Prior Knowledge in Text Classification
Druck et al. ge-fl proposed GE-FL to learn from labeled features using generalized expectation criteria. When given a set of labeled features $K$ , the reference distribution over classes of these features is denoted by $\hat{p}(y| x_k), k \in K$ . GE-FL introduces the divergence between this reference distribution and the model predicted distribution $p_\theta (y | x_k)$ , as a term of the objective function: 
Question: How do they define robustness of a model?
Answer: ability to accurately classify texts even when the amount of prior knowledge for different classes is unbalanced, and when the class distribution of the dataset is unbalanced
Low sensitivity to bias in prior knowledge
Paper: A Portuguese Native Language Identification Dataset
Several learner corpora have been compiled for English, such as the International Corpus of Learner English BIBREF0 . The importance of such resources has been increasingly recognized across a variety of research areas, from Second Language Acquisition to Natural Language Processing. Recently, we have seen substantial growth in this area and new corpora for languages other than English have appeared. For Romance languages, there are a several corpora and resources for French, Spanish BIBREF1 , and Italian BIBREF2 .
Question: Are the annotations automatic or manually created?
Answer: Automatic
Paper: A Portuguese Native Language Identification Dataset
NLI has attracted a lot of attention in recent years. Due to the availability of suitable data, as discussed earlier, this attention has been particularly focused on English. The most notable examples are the two editions of the NLI shared task organized in 2013 BIBREF6 and 2017 BIBREF7 .
Question: Do the errors of the model reflect linguistic similarity between different L1s?
Paper: A Portuguese Native Language Identification Dataset
The data was collected from three different learner corpora of Portuguese: (i) COPLE2; (ii) Leiria corpus, and (iii) PEAPL2 as presented in Table 1 .
Question: Is the dataset balanced between speakers of different L1s?
Paper: A Portuguese Native Language Identification Dataset
As demonstrated earlier, these learner corpora use different formats. COPLE2 is mainly codified in XML, although it gives the possibility of getting the student version of the essay in TXT format. PEAPL2 and Leiria corpus are compiled in TXT format. In both corpora, the TXT files contain the student version with special annotations from the transcription. For the NLI experiments we were interested in a clean txt version of the students' text, together with versions annotated at different linguistics levels. Therefore, as a first step, we removed all the annotations corresponding to the transcription process in PEAPL2 and Leiria files. As a second step, we proceeded to the linguistic annotation of the texts using different NLP tools.
Question: How long are the essays on average?
Answer: 204 tokens
Paper: Knowledge Graph Representation with Jointly Structural and Textual Encoding
Knowledge graphs have been proved to benefit many artificial intelligence applications, such as relation extraction, question answering and so on. A knowledge graph consists of multi-relational data, having entities as nodes and relations as edges. An instance of fact is represented as a triplet (Head Entity, Relation, Tail Entity), where the Relation indicates a relationship between these two entities. In the past decades, great progress has been made in building large scale knowledge graphs, such as WordNet BIBREF0 , Freebase BIBREF1 . However, most of them have been built either collaboratively or semi-automatically and as a result, they often suffer from incompleteness and sparseness.
Question: How large are the textual descriptions of entities?
Paper: Knowledge Graph Representation with Jointly Structural and Textual Encoding
In this section, we briefly introduce the background knowledge about the knowledge graph embedding.
Question: What neural models are used to encode the text?
Answer: NBOW, LSTM, attentive LSTM
Paper: Knowledge Graph Representation with Jointly Structural and Textual Encoding
Given an entity in most of the existing knowledge bases, there is always an available corresponding text description with valuable semantic information for this entity, which can provide beneficial supplement for entity representation.
Question: What baselines are used for comparison?
Paper: Knowledge Graph Representation with Jointly Structural and Textual Encoding
A simple and intuitive method is the neural bag-of-words (NBOW) model, in which the representation of text can be generated by summing up its constituent word representations.
Question: What datasets are used to evaluate this paper?
Paper: Contextual Joint Factor Acoustic Embeddings
In recent years, word embeddings have been successfully used in natural language processing (NLP), the most commonly known models are Word2Vec BIBREF0 and GloveBIBREF1. The reasons for such success are manifold. One key attribute of embedding methods is that word embedding models take into account context information of words, thereby allowing a more compact and manageable representation for wordsBIBREF2, BIBREF3. The embeddings are widely applied in many downstream NLP tasks such as neural machine translation, dialogue system or text summarisation BIBREF4, BIBREF5, BIBREF6, as well as in language modelling for speech recognitionBIBREF7.
Question: Which approach out of two proposed in the paper performed better in experiments?
Paper: Contextual Joint Factor Acoustic Embeddings
Question: What classification baselines are used for comparison?
Paper: Contextual Joint Factor Acoustic Embeddings
Most interest in acoustic embeddings can be observed on acoustic word embeddings, i.e. projections that map word acoustics into a fixed size vector space. Objective functions are chosen to project different word realisations to close proximity in the embedding space. Different approaches were used in the literature - for both supervised and unsupervised learning. For the supervised case, BIBREF9 introduced a convolutional neural network (CNN) based acoustic word embedding system for speech recognition, where words that sound alike are nearby in Euclidean distance. In their work, a CNN is used to predict a word from the corresponding acoustic signal, the output of the bottleneck layer before the final softmax layer is taken to be the embedding for the corresponding word. Further work used different network architectures to obtain acoustic word embeddings: BIBREF10 introduces a recurrent neural network (RNN) based approach instead.
Question: What TIMIT datasets are used for testing?
Answer: Once split into 8 subsets (A-H), the test set used are blocks D+H and blocks F+H
Paper: Contextual Joint Factor Acoustic Embeddings
Context information plays a fundamental role in speech processing. Phonemes could be influenced by surrounding frames through coarticulationBIBREF19 - an effect caused by speed limitations and transitions in the movement of articulators. Normally directly neighbouring phonemes have important impact on the sound realisation. Inversely, the surrounding phonemes also provide strong constraints on the phoneme that can be chosen at any given point, subject to to lexical and language constraints. This effect is for example exploited in phoneme recognition, by use of phoneme $n$-gram modelsBIBREF20. Equivalently inter word dependency - derived from linguistic constraints - can be exploited, as is the case in computing word embeddings with the aforementioned word2vecBIBREF0 method. The situation differs for the global latent variables, such as speaker properties or acoustic environment information. Speaker properties remains constant - and environments can also be assumed stationary over longer periods of time. Hence these variables are common between among neighbouring frames and windows. Modelling context information is helpful for identifying such information BIBREF21.
Question: How does this approach compares to the state-of-the-art results on these tasks?
Paper: Joint Detection and Location of English Puns
There exists a class of language construction known as pun in natural language texts and utterances, where a certain word or other lexical items are used to exploit two or more separate meanings. It has been shown that understanding of puns is an important research question with various real-world applications, such as human-computer interaction BIBREF0 , BIBREF1 and machine translation BIBREF2 . Recently, many researchers show their interests in studying puns, like detecting pun sentences BIBREF3 , locating puns in the text BIBREF4 , interpreting pun sentences BIBREF5 and generating sentences containing puns BIBREF6 , BIBREF7 , BIBREF8 . A pun is a wordplay in which a certain word suggests two or more meanings by exploiting polysemy, homonymy, or phonological similarity to another sign, for an intended humorous or rhetorical effect. Puns can be generally categorized into two groups, namely heterographic puns (where the pun and its latent target are phonologically similar) and homographic puns (where the two meanings of the pun reflect its two distinct senses) BIBREF9 . Consider the following two examples:
Question: What state-of-the-art results are achieved?
Answer: F1 score of 92.19 on homographic pun detection, 80.19 on homographic pun location, 89.76 on heterographic pun detection.
for the homographic dataset F1 score of 92.19 and 80.19 on detection and location and for the heterographic dataset F1 score of 89.76 on detection
Paper: Joint Detection and Location of English Puns
We first design a simple tagging scheme consisting of two tags { INLINEFORM0 }:
Question: What baselines do they compare with?
Answer: They compare with the following models: by Pedersen (2017), by Pramanick and Das (2017), by Mikhalkova and Karyakin (2017),  by Vadehra (2017), Indurthi and Oota (2017), by Vechtomova (2017), by (Cai et al., 2018), and CRF.
Paper: Joint Detection and Location of English Puns
Neural models have shown their effectiveness on sequence labeling tasks BIBREF13 , BIBREF14 , BIBREF15 . In this work, we adopt the bidirectional Long Short Term Memory (BiLSTM) BIBREF16 networks on top of the Conditional Random Fields BIBREF17 (CRF) architecture to make labeling decisions, which is one of the classical models for sequence labeling. Our model architecture is illustrated in Figure FIGREF8 with a running example. Given a context/sentence INLINEFORM0 where INLINEFORM1 is the length of the context, we generate the corresponding tag sequence INLINEFORM2 based on our designed tagging schemes and the original annotations for pun detection and location provided by the corpora. Our model is then trained on pairs of INLINEFORM3 .
Question: What datasets are used in evaluation?
Answer: A homographic and heterographic benchmark datasets by BIBREF9.
Paper: Joint Detection and Location of English Puns
We evaluate our model on two benchmark datasets BIBREF9 . The homographic dataset contains 2,250 contexts, 1,607 of which contain a pun. The heterographic dataset consists of 1,780 contexts with 1,271 containing a pun. We notice there is no standard splitting information provided for both datasets. Thus we apply 10-fold cross validation. To make direct comparisons with prior studies, following BIBREF4 , we accumulated the predictions for all ten folds and calculate the scores in the end.
Question: What is the tagging scheme employed?
Answer: A new tagging scheme that tags the words before and after the pun as well as the pun words.