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Found 16 papers, 9 papers with code
Diversity driven Attention Model for Query-based Abstractive Summarization
Abstractive summarization aims to generate a shorter version of the document covering all the salient points in a compact and coherent fashion.
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Query-Based Extractive Summarization
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Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization
For example, while generating descriptions from a table, a human would attend to information at two levels: (i) the fields (macro level) and (ii) the values within the field (micro level).
A Mixed Hierarchical Attention based Encoder-Decoder Approach for Standard Table Summarization
Structured data summarization involves generation of natural language summaries from structured input data.
Towards a Better Metric for Evaluating Question Generation Systems
In particular, it is important to verify whether such metrics used for evaluating AQG systems focus on answerability of the generated question by preferring questions which contain all relevant information such as question type (Wh-types), entities, relations, etc.
Frustratingly Poor Performance of Reading Comprehension Models on Non-adversarial Examples
We believe that the non-adversarial dataset created as a part of this work would complement the research on adversarial evaluation and give a more realistic assessment of the ability of RC models.
ElimiNet: A Model for Eliminating Options for Reading Comprehension with Multiple Choice Questions
Specifically, it has gates which decide whether an option can be eliminated given the passage, question pair and if so it tries to make the passage representation orthogonal to this eliminated option (akin to ignoring portions of the passage corresponding to the eliminated option).
Let's Ask Again: Refine Network for Automatic Question Generation
It is desired that the generated question should be (i) grammatically correct (ii) answerable from the passage and (iii) specific to the given answer.
Towards Transparent and Explainable Attention Models
To make attention mechanisms more faithful and plausible, we propose a modified LSTM cell with a diversity-driven training objective that ensures that the hidden representations learned at different time steps are diverse.
Towards Interpreting BERT for Reading Comprehension Based QA
BERT and its variants have achieved state-of-the-art performance in various NLP tasks.
Untangle: Critiquing Disentangled Recommendations
Untangle gives control on critiquing recommendations based on users preferences, without sacrificing on recommendation accuracy.
The heads hypothesis: A unifying statistical approach towards understanding multi-headed attention in BERT
There are two main challenges with existing methods for classification: (a) there are no standard scores across studies or across functional roles, and (b) these scores are often average quantities measured across sentences without capturing statistical significance.
A Framework for Rationale Extraction for Deep QA models
As neural-network-based QA models become deeper and more complex, there is a demand for robust frameworks which can access a model's rationale for its prediction.
T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation
In this work, we study the usefulness of Abstract Meaning Representation (AMR) graph as the intermediate style agnostic representation.
ReTAG: Reasoning Aware Table to Analytic Text Generation
The task of table summarization involves generating text that both succinctly and accurately represents the table or a specific set of highlighted cells within a table.
On the weak link between importance and prunability of attention heads
Given the success of Transformer-based models, two directions of study have emerged: interpreting role of individual attention heads and down-sizing the models for efficiency.
