z-uo/qasper-squad huggingface dataset:
Paper: Leveraging Recurrent Neural Networks for Multimodal Recognition of Social Norm Violation in Dialog
Social norms are informal understandings that govern human behavior. They serve as the basis for our beliefs and expectations about others, and are instantiated in human-human conversation through verbal and nonverbal behaviors BIBREF0 , BIBREF1 . There is considerable body of work on modeling socially normative behavior in intelligent agent-based systems BIBREF2 , BIBREF3 , aiming to facilitate lifelike conversations with human users. Violating such social norms and impoliteness in the conversation, on the other hand, have also been demonstrated to positively affect certain aspects of the social interaction. For instance, BIBREF4 suggests impoliteness may challenge rapport in strangers but it is also an indicator of built relationship among friends. The literature on social psychology BIBREF5 shows that the task of managing interpersonal bond like rapport requires management of face which, in turn, relies on behavioral expectation, which are allied with social norms early in a relationship, and become more interpersonally determined as the relationship proceeds. BIBREF6 advanced the arguments by proposing that with the increasing knowledge of one another, more general norms may be purposely violated in order to accommodate each other's behavior expectation. Moreover, they proposed that such kind of social norm violation in fact reinforce the sense of in-group connectedness. Finally in BIBREF7 , the authors discovered the effect of temporally co-occurring smile and social norm violation that signal high interpersonal rapport. Thus, we believe that recognizing the phenomena of social norm violation in dialog can contribute important insights into understanding the interpersonal dynamics that unfold between the interlocutors.
Question: Does this paper propose a new task that others can try to improve performance on?
Answer: No, there has been previous work on recognizing social norm violation.
Paper: Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge
Semantic parsing is the task of mapping a phrase in natural language onto a formal query in some fixed schema, which can then be executed against a knowledge base (KB) BIBREF0 , BIBREF1 . For example, the phrase “Who is the president of the United States?” might be mapped onto the query $\lambda (x).$ $\textsc {/government/president\_of}$ ( $x$ , $\textsc {USA}$ ), which, when executed against Freebase BIBREF2 , returns $\textsc {Barack Obama}$ . By mapping phrases to executable statements, semantic parsers can leverage large, curated sources of knowledge to answer questions BIBREF3 .
Question: What knowledge base do they use?
Paper: Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge
In this section, we briefly describe the current state-of-the-art model for open vocabulary semantic parsing, introduced by Krishnamurthy and Mitchell krishnamurthy-2015-semparse-open-vocabulary. Instead of mapping text to Freebase queries, as done by a traditional semantic parser, their method parses text to a surface logical form with predicates derived directly from the words in the text (see Figure 1 ). Next, a distribution over denotations for each predicate is learned using a matrix factorization approach similar to that of Riedel et al. riedel-2013-mf-universal-schema. This distribution is concisely represented using a probabilistic database, which also enables efficient probabilistic execution of logical form queries.
Question: How big is their dataset?
Answer: 3 million webpages processed with a CCG parser for training, 220 queries for development, and 307 queries for testing
Paper: Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge
Our key insight is that the executable queries used by traditional semantic parsers can be converted into features that provide KB information to the execution models of open vocabulary semantic parsers. Here we show how this is done.
Question: What task do they evaluate on?
Answer: Fill-in-the-blank natural language questions
Paper: Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling
State-of-the-art models for natural language processing (NLP) tasks like translation, question answering, and parsing include components intended to extract representations for the meaning and contents of each input sentence. These sentence encoder components are typically trained directly for the target task at hand. This approach can be effective on data rich tasks and yields human performance on some narrowly-defined benchmarks BIBREF1 , BIBREF2 , but it is tenable only for the few NLP tasks with millions of examples of training data. This has prompted interest in pretraining for sentence encoding: There is good reason to believe it should be possible to exploit outside data and training signals to effectively pretrain these encoders, both because they are intended to primarily capture sentence meaning rather than any task-specific skill, and because we have seen dramatic successes with pretraining in the related domains of word embeddings BIBREF3 and image encoders BIBREF4 .
Question: Do some pretraining objectives perform better than others for sentence level understanding tasks?
Paper: A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
Large-scale knowledge bases (KBs), such as YAGO BIBREF0 , Freebase BIBREF1 and DBpedia BIBREF2 , are usually databases of triples representing the relationships between entities in the form of fact (head entity, relation, tail entity) denoted as (h, r, t), e.g., (Melbourne, cityOf, Australia). These KBs are useful resources in many applications such as semantic searching and ranking BIBREF3 , BIBREF4 , BIBREF5 , question answering BIBREF6 , BIBREF7 and machine reading BIBREF8 . However, the KBs are still incomplete, i.e., missing a lot of valid triples BIBREF9 , BIBREF10 . Therefore, much research work has been devoted towards knowledge base completion or link prediction to predict whether a triple (h, r, t) is valid or not BIBREF11 .
Question: Did the authors try stacking multiple convolutional layers?
Paper: A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
A knowledge base $\mathcal {G}$ is a collection of valid factual triples in the form of (head entity, relation, tail entity) denoted as $(h, r, t)$ such that $h, t \in \mathcal {E}$ and $r \in \mathcal {R}$ where $\mathcal {E}$ is a set of entities and $\mathcal {R}$ is a set of relations. Embedding models aim to define a score function $f$ giving an implausibility score for each triple $(h, r, t)$ such that valid triples receive lower scores than invalid triples. Table 1 presents score functions in previous SOTA models.
Question: How many feature maps are generated for a given triple?
Answer: 3 feature maps for a given tuple
Paper: A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
We evaluate ConvKB on two benchmark datasets: WN18RR BIBREF30 and FB15k-237 BIBREF31 . WN18RR and FB15k-237 are correspondingly subsets of two common datasets WN18 and FB15k BIBREF13 . As noted by BIBREF31 , WN18 and FB15k are easy because they contain many reversible relations. So knowing relations are reversible allows us to easily predict the majority of test triples, e.g. state-of-the-art results on both WN18 and FB15k are obtained by using a simple reversal rule as shown in BIBREF30 . Therefore, WN18RR and FB15k-237 are created to not suffer from this reversible relation problem in WN18 and FB15k, for which the knowledge base completion task is more realistic. Table 2 presents the statistics of WN18RR and FB15k-237.
Question: How does the number of parameters compare to other knowledge base completion models?