z-uo/qasper-squad huggingface dataset:
Paper: Word Embeddings via Tensor Factorization
Some common word embeddings related to co-occurrence based matrix factorization include GloVe BIBREF7 , word2vec BIBREF9 , LexVec BIBREF10 , and NNSE BIBREF8 . In contrast, our work studies word embeddings given by factorization of tensors. An overview of tensor factorization methods is given in BIBREF11 .
Question: What are the main disadvantages of their proposed word embeddings?
Paper: Word Embeddings via Tensor Factorization
Throughout this paper we will write scalars in lowercase italics INLINEFORM0 , vectors in lowercase bold letters INLINEFORM1 , matrices with uppercase bold letters INLINEFORM2 , and tensors (of order INLINEFORM3 ) with Euler script notation INLINEFORM4 , as is standard in the literature.
Question: What dimensions of word embeddings do they produce using factorization?
Paper: Word Embeddings via Tensor Factorization
Pointwise mutual information (PMI) is a useful property in NLP that quantifies the likelihood that two words co-occur BIBREF9 . It is defined as: INLINEFORM0 
Question: On which dataset(s) do they compute their word embeddings?
Paper: Word Embeddings via Tensor Factorization
Just as the rank- INLINEFORM0 matrix decomposition is defined to be the product of two factor matrices ( INLINEFORM1 ), the canonical rank- INLINEFORM2 tensor decomposition for a third order tensor is defined to be the product of three factor matrices BIBREF11 : DISPLAYFORM0 
Question: Do they measure computation time of their factorizations compared to other word embeddings?
Paper: Voice Transformer Network: Sequence-to-Sequence Voice Conversion Using Transformer with Text-to-Speech Pretraining
Voice conversion (VC) aims to convert the speech from a source to that of a target without changing the linguistic content BIBREF0. Conventional VC systems follow an analysis—conversion —synthesis paradigm BIBREF1. First, a high quality vocoder such as WORLD BIBREF2 or STRAIGHT BIBREF3 is utilized to extract different acoustic features, such as spectral features and fundamental frequency (F0). These features are converted separately, and a waveform synthesizer finally generates the converted waveform using the converted features. Past VC studies have focused on the conversion of spectral features while only applying a simple linear transformation to F0. In addition, the conversion is usually performed frame-by-frame, i.e, the converted speech and the source speech are always of the same length. To summarize, the conversion of prosody, including F0 and duration, is overly simplified in the current VC literature.
Question: What datasets are experimented with?
Paper: Voice Transformer Network: Sequence-to-Sequence Voice Conversion Using Transformer with Text-to-Speech Pretraining
Seq2seq models are used to find a mapping between a source feature sequence $\vec{x}_{1:n}=(\vec{x}_1, \cdots , \vec{x}_n)$ and a target feature sequence $\vec{y}_{1:m}=(\vec{y}_1, \cdots , \vec{y}_m)$ which do not necessarily have to be of the same length, i.e, $n \ne m$. Most seq2seq models have an encoder—decoder structure BIBREF4, where advanced ones are equipped with an attention mechanism BIBREF5, BIBREF6. First, an encoder ($\text{Enc}$) maps $\vec{x}_{1:n}$ into a sequence of hidden representations ${1:n}=(1, \cdots , n)$. The decoding of the output sequence is autoregressive, which means that the previously generated symbols are considered an additional input at each decoding time step. To decode an output feature $\vec{y}_t$, a weighted sum of ${1:n}$ first forms a context vector $\vec{c}_t$, where the weight vector is represented by a calculated attention probability vector $\vec{a}_t=(a^{(1)}_t, \cdots , a^{(n)}_t)$. Each attention probability $a^{(k)}_t$ can be thought of as the importance of the hidden representation $k$ at the $t$th time step. Then the decoder ($\text{Dec}$) uses the context vector $\vec{c}$ and the previously generated features $\vec{y}_{1:t-1}=(\vec{y}_1, \cdots , \vec{y}_{t-1})$ to decode $\vec{y}_t$. Note that both the calculation of the attention vector and the decoding process take the previous hidden state of the decoder $\vec{q}_{t-1}$ as the input. The above-mentioned procedure can be formulated as follows: 1:n = Enc(x1:n),
Question: What is the baseline model?
Answer: a RNN-based seq2seq VC model called ATTS2S based on the Tacotron model
Paper: Towards Real-Time, Country-Level Location Classification of Worldwide Tweets
Social media are increasingly being used in the scientific community as a key source of data to help understand diverse natural and social phenomena, and this has prompted the development of a wide range of computational data mining tools that can extract knowledge from social media for both post-hoc and real time analysis. Thanks to the availability of a public API that enables the cost-free collection of a significant amount of data, Twitter has become a leading data source for such studies BIBREF0 . Having Twitter as a new kind of data source, researchers have looked into the development of tools for real-time trend analytics BIBREF1 , BIBREF2 or early detection of newsworthy events BIBREF3 , as well as into analytical approaches for understanding the sentiment expressed by users towards a target BIBREF4 , BIBREF5 , BIBREF6 , or public opinion on a specific topic BIBREF7 . However, Twitter data lacks reliable demographic details that would enable a representative sample of users to be collected and/or a focus on a specific user subgroup BIBREF8 , or other specific applications such as helping establish the trustworthiness of information posted BIBREF9 . Automated inference of social media demographics would be useful, among others, to broaden demographically aware social media analyses that are conducted through surveys BIBREF10 . One of the missing demographic details is a user's country of origin, which we study here. The only option then for the researcher is to try to infer such demographic characteristics before attempting the intended analysis.
Question: What model do they train?
Paper: Towards Real-Time, Country-Level Location Classification of Worldwide Tweets
A growing body of research deals with the automated inference of demographic details of Twitter users BIBREF8 . Researchers have attempted to infer attributes of Twitter users such as age BIBREF13 , BIBREF14 , gender BIBREF15 , BIBREF16 , BIBREF17 , BIBREF14 , political orientation BIBREF18 , BIBREF19 , BIBREF20 , BIBREF21 or a range of social identities BIBREF22 . Digging more deeply into the demographics of Twitter users, other researchers have attempted to infer socioeconomic demographics such as occupational class BIBREF23 , income BIBREF24 and socioeconomic status BIBREF25 . Work by Huang et al. BIBREF26 has also tried to infer the nationality of users; this work is different from that which we report here in that the country where the tweets were posted from, was already known.
Question: What are the eight features mentioned?
Paper: Towards Real-Time, Country-Level Location Classification of Worldwide Tweets
For training our classifier, we rely on the most widely adopted approach for the collection of a Twitter dataset with tweets categorised by location. This involves using the Twitter API endpoint that returns a stream of geolocated tweets posted from within one or more specified geographic bounding boxes. In our study, we set this bounding box to be the whole world (i.e., [-180,-90,180,90]) in order to retrieve tweets worldwide. This way, we collected streams of global geolocated tweets for two different week long periods: 4-11 October, 2014 (TC2014) and 22-28 October, 2015 (TC2015). This led to the collection of 31.7 million tweets in 2014 and 28.8 million tweets in 2015, which we adapt for our purposes as explained below.
Question: How many languages are considered in the experiments?
Paper: Open Information Extraction from Question-Answer Pairs
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Question: How did they evaluate the system?
Paper: Open Information Extraction from Question-Answer Pairs
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Question: Where did they get training data?
Answer: AmazonQA and ConciergeQA datasets
Paper: Open Information Extraction from Question-Answer Pairs
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Question: What extraction model did they use?
Answer: Multi-Encoder, Constrained-Decoder model
Paper: Open Information Extraction from Question-Answer Pairs
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Question: Which datasets did they experiment on?
Answer: ConciergeQA and AmazonQA
Paper: Open Information Extraction from Question-Answer Pairs
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Question: What types of facts can be extracted from QA pairs that can't be extracted from general text?
Paper: Flexibly-Structured Model for Task-Oriented Dialogues
A traditional task-oriented dialogue system is often composed of a few modules, such as natural language understanding, dialogue state tracking, knowledge base (KB) query, dialogue policy engine and response generation. Language understanding aims to convert the input to some predefined semantic frame. State tracking is a critical component that models explicitly the input semantic frame and the dialogue history for producing KB queries. The semantic frame and the corresponding belief state are defined in terms of informable slots values and requestable slots. Informable slot values capture information provided by the user so far, e.g., {price=cheap, food=italian} indicating the user wants a cheap Italian restaurant at this stage. Requestable slots capture the information requested by the user, e.g., {address, phone} means the user wants to know the address and phone number of a restaurant. Dialogue policy model decides on the system action which is then realized by a language generation component.
Question: How do slot binary classifiers improve performance?
Answer: by adding extra supervision to generate the slots that will be present in the response
Paper: Flexibly-Structured Model for Task-Oriented Dialogues
Our work is related to end-to-end task-oriented dialogue systems in general BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF14 , BIBREF7 , BIBREF8 and those that extend the Seq2Seq BIBREF15 architecture in particular BIBREF13 , BIBREF16 , BIBREF17 . Belief tracking, which is necessary to form KB queries, is not explicitly performed in the latter works. To compensate, BIBREF13 , BIBREF18 , BIBREF17 adopt a copy mechanism that allows copying information retrieved from the KB to the generated response. BIBREF16 adopt Memory Networks BIBREF19 to memorize the retrieved KB entities and words appearing in the dialogue history. These models scale linearly with the size of the KB and need to be retrained at each update of the KB. Both issues make these approaches less practical in real-world applications.
Question: What baselines have been used in this work?
Answer: NDM, LIDM, KVRN, and TSCP/RL
Paper: Latent Multi-task Architecture Learning
Multi-task learning (MTL) in deep neural networks is typically a result of parameter sharing between two networks (of usually the same dimensions) BIBREF0 . If you have two three-layered, recurrent neural networks, both with an embedding inner layer and each recurrent layer feeding the task-specific classifier function through a feed-forward neural network, we have 19 pairs of layers that could share parameters. With the option of having private spaces, this gives us $5^{19}=$ 19,073,486,328,125 possible MTL architectures. If we additionally consider soft sharing of parameters, the number of possible architectures grows infinite. It is obviously not feasible to search this space. Neural architecture search (NAS) BIBREF1 typically requires learning from a large pool of experiments with different architectures. Searching for multi-task architectures via reinforcement learning BIBREF2 or evolutionary approaches BIBREF3 can therefore be quite expensive. In this paper, we jointly learn a latent multi-task architecture and task-specific models, paying a minimal computational cost over single task learning and standard multi-task learning (5-7% training time). We refer to this problem as multi-task architecture learning. In contrast to architecture search, the overall meta-architecture is fixed and the model learns the optimal latent connections and pathways for each task. Recently, a few authors have considered multi-task architecture learning BIBREF4 , BIBREF5 , but these papers only address a subspace of the possible architectures typically considered in neural multi-task learning, while other approaches at most consider a couple of architectures for sharing BIBREF6 , BIBREF7 , BIBREF8 . In contrast, we introduce a framework that unifies previous approaches by introducing trainable parameters for all the components that differentiate multi-task learning approaches along the above dimensions.
Question: Do sluice networks outperform non-transfer learning approaches?
Paper: Latent Multi-task Architecture Learning
We introduce a meta-architecture for multi-task architecture learning, which we refer to as a sluice network, sketched in Figure 1 for the case of two tasks. The network learns to share parameters between $M$ neural networks—in our case, two deep recurrent neural networks (RNNs) BIBREF12 . The network can be seen as an end-to-end differentiable union of a set of sharing architectures with parameters controlling the sharing. By learning the weights of those sharing parameters (sluices) jointly with the rest of the model, we arrive at a task-specific MTL architecture over the course of training.
Question: What is hard parameter sharing?
Paper: Integration of Japanese Papers Into the DBLP Data Set
tocchapterList of Acronyms
Question: How successful are they at matching names of authors in Japanese and English?
Paper: Integration of Japanese Papers Into the DBLP Data Set
The idea for this work was born when the author was searching for a possibility to combine computer science with his minor subject Japan studies in his diploma thesis. After dismissing some ideas leaning towards Named Entity Recognition and computer linguistics the author chose “Integration of Japanese Papers Into the DBLP Data Set” as his subject. The DBLP is a well-known and useful tool for finding papers published in the context of computer science. The challenge to deal with such a huge database and the problems that occur when processing Japanese input data was the reason why this idea has been chosen. The hope is that, in the future, many Japanese papers can be added by the responsible people of the DBLP project.
Question: Is their approach applicable to papers outside computer science?
Paper: Integration of Japanese Papers Into the DBLP Data Set
Computer scientists are likely to use the DBLP to find information about certain papers or authors. Therefore, the DBLP is supposed to provide information about as many papers as possible. For example, one could be interested in the paper “Analysis of an Entry Term Set of a Civil Engineering Dictionary and Its Application to Information Retrieval Systems” by Akiko Aizawa et al. (2005) but DBLP does not include it yet. Japanese scientists might look for the original (Japanese) title “土木関連用語辞典の見出し語の分析と検索システムにおける活用に関する考察” or use Aizawa's name in Japanese characters (相澤彰子) for a search in DBLP. The DBLP contains the author “Akiko Aizawa” but does not contain this specific paper or the author's original name in Japanese characters. Our work is to implement a tool which addresses these questions, support the DBLP team in the integration of Japanese papers and reveal the difficulties of realizing the integration.
Question: Do they translate metadata from Japanese papers to English?
Paper: Evaluating the Performance of a Speech Recognition based System
There are several commercial menu based ASR systems available around the world for a significant number of languages and interestingly speech solution based on these ASR are being used with good success in the Western part of the globe BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . Typically, a menu based ASR system restricts user to speak from a pre-defined closed set of words for enabling a transaction. Before commercial deployment of a speech solution it is imperative to have a quantitative measure of the performance of the speech solution which is primarily based on the speech recognition accuracy of the speech engine used. Generally, the recognition performance of any speech recognition based solution is quantitatively evaluated by putting it to actual use by the people who are the intended users and then analyzing the logs to identify successful and unsuccessful transactions. This evaluation is then used to identifying any further improvement in the speech recognition based solution to better the overall transaction completion rates. This process of evaluation is both time consuming and expensive. For evaluation one needs to identify a set of users and also identify the set of actual usage situations and perform the test. It is also important that the set of users are able to use the system with ease meaning that even in the test conditions the performance of the system, should be good, while this can not usually be guaranteed this aspect of keeping the user experience good makes it necessary to employ a wizard of Oz (WoZ) approach. Typically this requires a human agent in the loop during actual speech transaction where the human agent corrects any mis-recognition by actually listening to the conversation between the human user and the machine without the user knowing that there is a human agent in the loop. The use of WoZ is another expense in the testing a speech solution. All this makes testing a speech solution an expensive and time consuming procedure.
Question: what bottlenecks were identified?
Answer: Confusion in recognizing the words that are active at a given node by a speech recognition solution developed for Indian Railway Inquiry System.
Paper: Systematic Generalization: What Is Required and Can It Be Learned?
In recent years, neural network based models have become the workhorse of natural language understanding and generation. They empower industrial systems in machine translation BIBREF0 and text generation BIBREF1 , also showing state-of-the-art performance on numerous benchmarks including Recognizing Textual Entailment (RTE) BIBREF2 , Visual Question Answering (VQA) BIBREF3 , and Reading Comprehension BIBREF4 . Despite these successes, a growing body of literature suggests that these approaches do not generalize outside of the specific distributions on which they are trained, something that is necessary for a language understanding system to be widely deployed in the real world. Investigations on the three aforementioned tasks have shown that neural models easily latch onto statistical regularities which are omnipresent in existing datasets BIBREF5 , BIBREF6 , BIBREF7 and extremely hard to avoid in large scale data collection. Having learned such dataset-specific solutions, neural networks fail to make correct predictions for examples that are even slightly out of domain, yet are trivial for humans. These findings have been corroborated by a recent investigation on a synthetic instruction-following task BIBREF8 , in which seq2seq models BIBREF9 , BIBREF10 have shown little systematicity BIBREF11 in how they generalize, that is they do not learn general rules on how to compose words and fail spectacularly when for example asked to interpret “jump twice” after training on “jump”, “run twice” and “walk twice”.
Question: What is grounded language understanding?
Paper: Char-RNN and Active Learning for Hashtag Segmentation
A hashtag is a form of metadata labeling used in various social networks to help the users to navigate through the content. For example, one of the most popular hashtags on Instagram is "#photooftheday" [photo of the day]. Hashtags are written without any delimiters, although some users use an underscore or camel-casing to separate words. Hashtags themselves may be a great source for features for following opinion mining and social network analysis. Basically hashtags serve as keyphrases for a post in social media. By segmenting the hashtags into separate words we may use regular techniques to process them. The problem of hashtag segmentation resembles of another problem, namely word segmentation.
Question: Does the paper report the performance on the task of a Neural Machine Translation model?
Paper: Char-RNN and Active Learning for Hashtag Segmentation
We treat hashtag segmentation as a sequence labeling task. Each character is labeled with one of the labels $\mathcal {L} = \lbrace 0, 1\rbrace $, (1) for the end of a word, and (0) otherwise (Table TABREF9 and TABREF9). Given a string $s = {s_1, \ldots , s_n}$ of characters, the task is to find the labels $Y^* = {y_1^*. \ldots , y_n^*}$, such that $ Y^* = \arg \max _{Y \in \mathcal {L} ^n} p(Y | s).$
Question: What are the predefined morpho-syntactic patterns used to filter the training data?
Paper: Char-RNN and Active Learning for Hashtag Segmentation
In this section we describe the datasets we used for hashtag segmentation. We experimented with Russian and English datasets to compare the performance of the char-RNN.
Question: Is the RNN model evaluated against any baseline?