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AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models
TLDR
This work introduces AllenNLP Interpret, a flexible framework for interpreting NLP models, which provides interpretation primitives for anyAllenNLP model and task, a suite of built-in interpretation methods, and a library of front-end visualization components. Expand
Evaluating Models’ Local Decision Boundaries via Contrast Sets
TLDR
A more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data, and recommends that the dataset authors manually perturb the test instances in small but meaningful ways that (typically) change the gold label, creating contrast sets. Expand
An Improved Neural Baseline for Temporal Relation Extraction
TLDR
A new neural system is proposed that achieves about 10% absolute improvement in accuracy over the previous best system (25% error reduction) on two benchmark datasets and could serve as a strong baseline for future research in this area. Expand
Evaluating NLP Models via Contrast Sets
TLDR
A new annotation paradigm for NLP is proposed that helps to close systematic gaps in the test data, and it is recommended that after a dataset is constructed, the dataset authors manually perturb the test instances in small but meaningful ways that change the gold label, creating contrast sets. Expand
Obtaining Faithful Interpretations from Compositional Neural Networks
TLDR
It is found that the intermediate outputs of NMNs differ from the expected output, illustrating that the network structure does not provide a faithful explanation of model behaviour, and particular choices for module architecture are proposed that yield much better faithfulness, at a minimal cost to accuracy. Expand
Improving Generalization in Coreference Resolution via Adversarial Training
TLDR
This work uses the technique of adversarial gradient-based training to retrain the state-of-the-art system and demonstrates that the retrained system achieves higher performance on the CoNLL dataset (both with and without the change of named entities) and the GAP dataset. Expand
Correlation Clustering with Same-Cluster Queries Bounded by Optimal Cost
TLDR
This paper presents an efficient algorithm that recovers an exact optimal clustering using at most $2C_{OPT} $ queries and an efficient algorithms that outputs a $2-approximation using at least two queries, both of which are efficient against several known correlation clustering algorithms. Expand
Evaluation of named entity coreference
TLDR
New metrics for evaluating named entity coreference are introduced that address discrepancies and show that for the comparisons of competitive systems, standard coreference evaluations could give misleading results for this task. Expand
Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering
TLDR
This work proposes a model that computes a representation and denotation for all question spans in a bottom-up, compositional manner using a CKY-style parser, and shows that this inductive bias towards tree structures dramatically improves systematic generalization to out-of- distribution examples. Expand
Analyzing Compositionality in Visual Question Answering
TLDR
This paper analyzes the performance of one of transformer models pretrained on large amounts of images and associated text, LXMERT, and shows that despite the model’s strong quantitative results, it may not be performing compositional reasoning because it does not need many relational cues to achieve this performance and more generally uses relatively little linguistic information. Expand
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