z-uo/qasper-squad huggingface dataset:
Paper: Natural Language State Representation for Reinforcement Learning
“The world of our experiences must be enormously simplified and generalized before it is possible to make a symbolic inventory of all our experiences of things and relations."
Question: What result from experiments suggest that natural language based agents are more robust?
Answer: Average reward across 5 seeds show that NLP representations are robust to changes in the environment as well task-nuisances
Paper: Natural Language State Representation for Reinforcement Learning
In Reinforcement Learning the goal is to learn a policy $\pi (s)$, which is a mapping from state $s$ to a probability distribution over actions $\mathcal {A}$, with the objective to maximize a reward $r(s)$ that is provided by the environment. This is often solved by formulating the problem as a Markov Decision Process (MDP) BIBREF19. Two common quantities used to estimate the performance in MDPs are the value $v (s)$ and action-value $Q (s, a)$ functions, which are defined as follows: ${v(s) = \mathbb {E}^{\pi } [\sum _t \gamma ^t r_t | s_0 = s ]}$ and ${Q(s, a) = \mathbb {E}^{\pi } [\sum _t \gamma ^t r_t | s_0 = s, a_0 = a ]}$. Two prominent algorithms for solving RL tasks, which we use in this paper, are the value-based DQN BIBREF2 and the policy-based PPO BIBREF3.
Question: How better is performance of natural language based agents in experiments?
Paper: Natural Language State Representation for Reinforcement Learning
A word embedding is a mapping from a word $w$ to a vector $\mathbf {w} \in \mathbb {R}^d$. A simple form of word embedding is the Bag of Words (BoW), a vector $\mathbf {w} \in \mathbb {N}^{|D|}$ ($|D|$ is the dictionary size), in which each word receives a unique 1-hot vector representation. Recently, more efficient methods have been proposed, in which the embedding vector is smaller than the dictionary size, $d \ll |D|$. These methods are also known as distributional embeddings.
Question: How much faster natural language agents converge in performed experiments?
Paper: Natural Language State Representation for Reinforcement Learning
Contemporary methods for semantic representation of states currently follow one of three approaches: (1) raw visual inputs BIBREF2, BIBREF26, in which raw sensory values of pixels are used from one or multiple sources, (2) feature vectors BIBREF27, BIBREF28, in which general features of the problem are chosen, with no specific structure, and (3) semantic segmentation maps BIBREF29, BIBREF30, in which discrete or logical values are used in one or many channels to represent the general features of the state.
Question: What experiments authors perform?
Paper: Natural Language State Representation for Reinforcement Learning
In this section we compare the different types of semantic representations for representing states in the ViZDoom environment BIBREF26, as described in the previous section. More specifically, we use a semantic natural language parser in order to describe a state, over numerous instances of levels varying in difficulty, task-nuisances, and objectives. Our results show that, though semantic segmentation and feature vector representation techniques express a similar statistic of the state, natural language representation offers better performance, faster convergence, more robust solutions, as well as better transfer.
Question: How is state to learn and complete tasks represented via natural language?
Paper: Query-oriented text summarization based on hypergraph transversals
The development of automatic tools for the summarization of large corpora of documents has attracted a widespread interest in recent years. With fields of application ranging from medical sciences to finance and legal science, these summarization systems considerably reduce the time required for knowledge acquisition and decision making, by identifying and formatting the relevant information from a collection of documents. Since most applications involve large corpora rather than single documents, summarization systems developed recently are meant to produce summaries of multiple documents. Similarly, the interest has shifted from generic towards query-oriented summarization, in which a query expresses the user's needs. Moreover, existing summarizers are generally extractive, namely they produce summaries by extracting relevant sentences from the original corpus.
Question: How does the model compare with the MMR baseline?
Paper: Text-based inference of moral sentiment change
People's moral sentiment—our feelings toward right or wrong—can change over time. For instance, the public's views toward slavery have shifted substantially over the past centuries BIBREF0. How society's moral views evolve has been a long-standing issue and a constant source of controversy subject to interpretations from social scientists, historians, philosophers, among others. Here we ask whether natural language processing has the potential to inform moral sentiment change in society at scale, involving minimal human labour or intervention.
Question: Does the paper discuss previous models which have been applied to the same task?
Paper: Text-based inference of moral sentiment change
An emerging body of work in natural language processing and computational social science has investigated how NLP systems can detect moral sentiment in online text. For example, moral rhetoric in social media and political discourse BIBREF19, BIBREF20, BIBREF21, the relation between moralization in social media and violent protests BIBREF22, and bias toward refugees in talk radio shows BIBREF23 have been some of the topics explored in this line of inquiry. In contrast to this line of research, the development of a formal framework for moral sentiment change is still under-explored, with no existing systematic and formal treatment of this topic BIBREF16.
Question: Which datasets are used in the paper?
Answer: Google N-grams
COHA
Moral Foundations Dictionary (MFD)
Paper: Text-based inference of moral sentiment change
Our framework treats the moral sentiment toward a concept at three incremental levels, as illustrated in Figure FIGREF3. First, we consider moral relevance, distinguishing between morally irrelevant and morally relevant concepts. At the second tier, moral polarity, we further split morally relevant concepts into those that are positively or negatively perceived in the moral domain. Finally, a third tier classifies these concepts into fine-grained categories of human morality.
Question: How does the parameter-free model work?
Paper: Text-based inference of moral sentiment change
To ground moral sentiment in text, we leverage the Moral Foundations Dictionary BIBREF27. The MFD is a psycholinguistic resource that associates each MFT category with a set of seed words, which are words that provide evidence for the corresponding moral category in text. We use the MFD for moral polarity classification by dividing seed words into positive and negative sets, and for fine-grained categorization by splitting them into the 10 MFT categories.
Question: How do they quantify moral relevance?
Answer: By complementing morally relevant seed words with a set of morally irrelevant seed words based on the notion of valence
Paper: Text-based inference of moral sentiment change
We propose and evaluate a set of probabilistic models to classify concepts in the three tiers of morality specified above. Our models exploit the semantic structure of word embeddings BIBREF29 to perform tiered moral classification of query concepts. In each tier, the model receives a query word embedding vector $\mathbf {q}$ and a set of seed words for each class in that tier, and infers the posterior probabilities over the set of classes $c$ to which the query concept is associated with.
Question: Which fine-grained moral dimension examples do they showcase?
Paper: Text-based inference of moral sentiment change
To apply our models diachronically, we require a word embedding space that captures the meanings of words at different points in time and reflects changes pertaining to a particular word as diachronic shifts in a common embedding space.
Question: Which dataset sources to they use to demonstrate moral sentiment through history?
Paper: Bringing Stories Alive: Generating Interactive Fiction Worlds
Interactive fictions—also called text-adventure games or text-based games—are games in which a player interacts with a virtual world purely through textual natural language—receiving descriptions of what they “see” and writing out how they want to act, an example can be seen in Figure FIGREF2. Interactive fiction games are often structured as puzzles, or quests, set within the confines of given game world. Interactive fictions have been adopted as a test-bed for real-time game playing agents BIBREF0, BIBREF1, BIBREF2. Unlike other, graphical games, interactive fictions test agents' abilities to infer the state of the world through communication and to indirectly affect change in the world through language. Interactive fictions are typically modeled after real or fantasy worlds; commonsense knowledge is an important factor in successfully playing interactive fictions BIBREF3, BIBREF4.
Question: How well did the system do?
Paper: Bringing Stories Alive: Generating Interactive Fiction Worlds
There has been a slew of recent work in developing agents that can play text games BIBREF0, BIBREF5, BIBREF1, BIBREF6. BIBREF7 ammanabrolutransfer,ammanabrolu,ammanabrolu2020graph in particular use knowledge graphs as state representations for game-playing agents. BIBREF8 propose QAit, a set of question answering tasks framed as text-based or interactive fiction games. QAit focuses on helping agents learn procedural knowledge through interaction with a dynamic environment. These works all focus on agents that learn to play a given set of interactive fiction games as opposed to generating them.
Question: How is the information extracted?
Paper: Generating Classical Chinese Poems from Vernacular Chinese
During thousands of years, millions of classical Chinese poems have been written. They contain ancient poets' emotions such as their appreciation for nature, desiring for freedom and concerns for their countries. Among various types of classical poetry, quatrain poems stand out. On the one hand, their aestheticism and terseness exhibit unique elegance. On the other hand, composing such poems is extremely challenging due to their phonological, tonal and structural restrictions.
Question: What are some guidelines in writing input vernacular so model can generate
Paper: Generating Classical Chinese Poems from Vernacular Chinese
Classical Chinese Poem Generation Most previous works in classical Chinese poem generation focus on improving the semantic coherence of generated poems. Based on LSTM, Zhang and Lapata Zhang2014ChinesePG purposed generating poem lines incrementally by taking into account the history of what has been generated so far. Yan Yan2016iPA proposed a polishing generation schema, each poem line is generated incrementally and iteratively by refining each line one-by-one. Wang et al. Wang2016ChinesePG and Yi et al. Yi2018ChinesePG proposed models to keep the generated poems coherent and semantically consistent with the user's intent. There are also researches that focus on other aspects of poem generation. (Yang et al. Yang2018StylisticCP explored increasing the diversity of generated poems using an unsupervised approach. Xu et al. Xu2018HowII explored generating Chinese poems from images. While most previous works generate poems based on topic words, our work targets at a novel task: generating poems from vernacular Chinese paragraphs.
Question: How much is proposed model better in perplexity and BLEU score than typical UMT models?
Answer: Perplexity of the best model is 65.58 compared to best baseline 105.79.
Bleu of the best model is 6.57 compared to best baseline 5.50.
Paper: Generating Classical Chinese Poems from Vernacular Chinese
We transform our poem generation task as an unsupervised machine translation problem. As illustrated in Figure FIGREF1, based on the recently proposed UMT framework BIBREF2, our model is composed of the following components:
Question: What dataset is used for training?
Paper: Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever
Task-oriented dialogue system, which helps users to achieve specific goals with natural language, is attracting more and more research attention. With the success of the sequence-to-sequence (Seq2Seq) models in text generation BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, several works tried to model the task-oriented dialogue as the Seq2Seq generation of response from the dialogue history BIBREF5, BIBREF6, BIBREF7. This kind of modeling scheme frees the task-oriented dialogue system from the manually designed pipeline modules and heavy annotation labor for these modules.
Question: What were the evaluation metrics?
Paper: Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever
In this section, we will describe the input and output of the end-to-end task-oriented dialogue system, and the definition of Seq2Seq task-oriented dialogue generation.
Question: What were the baseline systems?
Paper: Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever
Given a dialogue between a user ($u$) and a system ($s$), we follow eric:2017:SIGDial and represent the $k$-turned dialogue utterances as $\lbrace (u_{1}, s_{1} ), (u_{2} , s_{2} ), ... , (u_{k}, s_{k})\rbrace $. At the $i^{\text{th}}$ turn of the dialogue, we aggregate dialogue context which consists of the tokens of $(u_{1}, s_{1}, ..., s_{i-1}, u_{i})$ and use $\mathbf {x} = (x_{1}, x_{2}, ..., x_{m})$ to denote the whole dialogue history word by word, where $m$ is the number of tokens in the dialogue history.
Question: Which dialog datasets did they experiment with?
Paper: Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever
In this paper, we assume to have the access to a relational-database-like KB $B$, which consists of $|\mathcal {R}|$ rows and $|\mathcal {C}|$ columns. The value of entity in the $j^{\text{th}}$ row and the $i^{\text{th}}$ column is noted as $v_{j, i}$.
Question: What KB is used?
Paper: From FiLM to Video: Multi-turn Question Answering with Multi-modal Context
Deep neural networks have been successfully applied to several computer vision tasks such as image classification BIBREF0 , object detection BIBREF1 , video action classification BIBREF2 , etc. They have also been successfully applied to natural language processing tasks such as machine translation BIBREF3 , machine reading comprehension BIBREF4 , etc. There has also been an explosion of interest in tasks which combine multiple modalities such as audio, vision, and language together. Some popular multi-modal tasks combining these three modalities, and their differences are highlighted in Table TABREF1 .
Question: At which interval do they extract video and audio frames?
Paper: From FiLM to Video: Multi-turn Question Answering with Multi-modal Context
With the availability of large conversational corpora from sources like Reddit and Twitter, there has been a lot of recent work on end-to-end modelling of dialogue for open domains. BIBREF12 treated dialogue as a machine translation problem where they translate from the stimulus to the response. They observed this to be more challenging than machine translation tasks due the larger diversity of possible responses. Among approaches that just use the previous utterance to generate the current response, BIBREF13 proposed a response generation model based on the encoder decoder framework. BIBREF14 also proposed an encoder-decoder based neural network architecture that uses the previous two utterances to generate the current response. Among discriminative methods (i.e. methods that produce a score for utterances from a set and then rank them), BIBREF15 proposed a neural architecture to select the best next response from a list of responses by measuring their similarity to the dialogue context. BIBREF16 extended prior work on encoder-decoder-based models to multi-turn conversations. They trained a hierarchical model called hred for generating dialogue utterances where a recurrent neural network encoder encodes each utterance. A higher-level recurrent neural network maintains the dialogue state by further encoding the individual utterance encodings. This dialogue state is then decoded by another recurrent decoder to generate the response at that point in time. In followup work, BIBREF17 used a latent stochastic variable to condition the generation process which aided their model in producing longer coherent outputs that better retain the context.
Question: Do they use pretrained word vectors for dialogue context embedding?
Paper: From FiLM to Video: Multi-turn Question Answering with Multi-modal Context
The avsd dataset BIBREF28 consists of dialogues collected via amt. Each dialogue is associated with a video from the Charades BIBREF29 dataset and has conversations between two amt workers related to the video. The Charades dataset has multi-action short videos and it provides text descriptions for these videos, which the avsd challenge also distributes as the caption. The avsd dataset has been collected using similar methodology as the visdial dataset. In avsd, each dialogue turn consists of a question and answer pair. One of the amt workers assumes the role of questioner while the other amt worker assumes the role of answerer. The questioner sees three static frames from the video and has to ask questions. The answerer sees the video and answers the questions asked by the questioner. After 10 such qa turns, the questioner wraps up by writing a summary of the video based on the conversation.
Question: Do they train a different training method except from scheduled sampling?
Answer: Answer with content missing: (list missing) 
Scheduled sampling: In our experiments, we found that models trained with scheduled sampling performed better (about 0.004 BLEU-4 on validation set) than the ones trained using teacher-forcing for the AVSD dataset. Hence, we use scheduled sampling for all the results we report in this paper.

Yes.
Paper: Civique: Using Social Media to Detect Urban Emergencies
With the surge in the use of social media, micro-blogging sites like Twitter, Facebook, and Foursquare have become household words. Growing ubiquity of mobile phones in highly populated developing nations has spurred an exponential rise in social media usage. The heavy volume of social media posts tagged with users' location information on micro-blogging website Twitter presents a unique opportunity to scan these posts. These Short texts (e.g. "tweets") on social media contain information about various events happening around the globe, as people post about events and incidents alike. Conventional web outlets provide emergency phone numbers (i.e. 100, 911), etc., and are fast and accurate. Our system, on the other hand, connects its users through a relatively newer platform i.e. social media, and provides an alternative to these conventional methods. In case of their failure or when such means are busy/occupied, an alternative could prove to be life saving.
Question: Is the web interface publicly accessible?
Paper: Civique: Using Social Media to Detect Urban Emergencies
In 2015, INLINEFORM0 of all unnatural deaths in India were caused by accidents, and INLINEFORM1 by accidental fires. Moreover, the Indian subcontinent suffered seven earthquakes in 2015, with the recent Nepal earthquake alone killing more than 9000 people and injuring INLINEFORM2 . We believe we can harness the current social media activity on the web to minimize losses by quickly connecting affected people and the concerned authorities. Our work is motivated by the following factors, (a) Social media is very accessible in the current scenario. (The “Digital India” initiative by the Government of India promotes internet activity, and thus a pro-active social media.) (b) As per the Internet trends reported in 2014, about 117 million Indians are connected to the Internet through mobile devices. (c) A system such as ours can point out or visualize the affected areas precisely and help inform the authorities in a timely fashion. (d) Such a system can be used on a global scale to reduce the effect of natural calamities and prevent loss of life.
Question: Is the Android application publicly available?
Paper: Civique: Using Social Media to Detect Urban Emergencies
We propose a software architecture for Emergency detection and visualization as shown in figure FIGREF9 . We collect data using Twitter API, and perform language pre-processing before applying a classification model. Tweets are labelled manually with <emergency>and <non-emergency>labels, and later classified manually to provide labels according to the type of emergency they indicate. We use the manually labeled data for training our classifiers.
Question: What classifier is used for emergency categorization?
Paper: Civique: Using Social Media to Detect Urban Emergencies
We implement a cleaning module to automate the cleaning of tweets obtained from the Twitter API. We remove URLs, special symbols like @ along with the user mentions, Hashtags and any associated text. We also replace special symbols by blank spaces, and inculcate the module as shown in figure FIGREF9 .
Question: What classifier is used for emergency detection?
Paper: Civique: Using Social Media to Detect Urban Emergencies
The first classifier model acts as a filter for the second stage of classification. We use both SVM and NB to compare the results and choose SVM later for stage one classification model, owing to a better F-score. The training is performed on tweets labeled with classes <emergency>, and <non-emergency> based on unigrams as features. We create word vectors of strings in the tweet using a filter available in the WEKA API BIBREF9 , and perform cross validation using standard classification techniques.
Question: Do the tweets come from any individual?
Paper: Civique: Using Social Media to Detect Urban Emergencies
We employ a multi-class Naive Bayes classifier as the second stage classification mechanism, for categorizing tweets appropriately, depending on the type of emergencies they indicate. This multi-class classifier is trained on data manually labeled with classes. We tokenize the training data using “NgramTokenizer” and then, apply a filter to create word vectors of strings before training. We use “trigrams” as features to build a model which, later, classifies tweets into appropriate categories, in real time. We then perform cross validation using standard techniques to calculate the results, which are shown under the label “Stage 2”, in table TABREF20 .
Question: How many categories are there?
Paper: Civique: Using Social Media to Detect Urban Emergencies
We use Google Maps Geocoding API to display the possible location of the tweet origin based on longitude and latitude. Our visualizer presents the user with a map and pinpoints the location with custom icons for earthquake, cyclone, fire accident etc. Since we currently collect tweets with a location filter for the city of "Mumbai", we display its map location on the interface. The possible occurrences of such incidents are displayed on the map as soon as our system is able to detect it.
Question: What was the baseline?
Paper: Civique: Using Social Media to Detect Urban Emergencies
We evaluate our system using automated, and manual evaluation techniques. We perform 10-fold cross validation to obtain the F-scores for our classification systems. We use the following technique for dataset creation. We test the system in realtime environments, and tweet about fires at random locations in our city, using test accounts. Our system was able to detect such tweets and detect them with locations shown on the map.
Question: Are the tweets specific to a region?