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Found 30 papers, 19 papers with code
Evaluating Coreference Resolvers on Community-based Question Answering: From Rule-based to State of the Art
The results of our extrinsic evaluation show that while there is a significant difference between the performance of the rule-based system vs. state-of-the-art neural model on coreference resolution datasets, we do not observe a considerable difference on their impact on downstream models.
The Universal Anaphora Scorer
It also supports the evaluation of split antecedent anaphora and discourse deixis, for which no tools existed.
FERMAT: An Alternative to Accuracy for Numerical Reasoning
Apart from providing a comprehensive evaluation of models on different numerical reasoning aspects, FERMAT enables a systematic and automated generation of an arbitrarily large training or evaluation set for each aspect. The datasets and codes are publicly available to generate further multi-view data for ulterior tasks and languages.
Lessons Learned from a Citizen Science Project for Natural Language Processing
Citizen Science is an alternative to crowdsourcing that is relatively unexplored in the context of NLP.
Layer or Representation Space: What makes BERT-based Evaluation Metrics Robust?
The evaluation of recent embedding-based evaluation metrics for text generation is primarily based on measuring their correlation with human evaluations on standard benchmarks.
Transformers with Learnable Activation Functions
In contrast to conventional, predefined activation functions, RAFs can adaptively learn optimal activation functions during training according to input data.
Scoring Coreference Chains with Split-Antecedent Anaphors
Anaphoric reference is an aspect of language interpretation covering a variety of types of interpretation beyond the simple case of identity reference to entities introduced via nominal expressions covered by the traditional coreference task in its most recent incarnation in ONTONOTES and similar datasets.
Improving the Numerical Reasoning Skills of Pretrained Language Models
We evaluate our approach on three different tasks that require numerical reasoning, including (a) reading comprehension in the DROP dataset, (b) inference-on-tables in the InfoTabs dataset, and (c) table-to-text generation in WikiBio and SciGen datasets.
Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization
In this work, we show that NLI models can be effective for this task when the training data is augmented with high-quality task-oriented examples.
Abstractive Text Summarization
Natural Language Inference
+1
Adaptable Adapters
The resulting adapters (a) contain about 50% of the learning parameters of the standard adapter and are therefore more efficient at training and inference, and require less storage space, and (b) achieve considerably higher performances in low-data settings.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning
Recent prompt-based approaches allow pretrained language models to achieve strong performances on few-shot finetuning by reformulating downstream tasks as a language modeling problem.
Learning to Reason for Text Generation from Scientific Tables
In this paper, we introduce SciGen, a new challenge dataset for the task of reasoning-aware data-to-text generation consisting of tables from scientific articles and their corresponding descriptions.
Stay Together: A System for Single and Split-antecedent Anaphora Resolution
Split-antecedent anaphora is rarer and more complex to resolve than single-antecedent anaphora; as a result, it is not annotated in many datasets designed to test coreference, and previous work on resolving this type of anaphora was carried out in unrealistic conditions that assume gold mentions and/or gold split-antecedent anaphors are available.
Coreference Reasoning in Machine Reading Comprehension
We propose a methodology for creating MRC datasets that better reflect the challenges of coreference reasoning and use it to create a sample evaluation set.
Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution
One limitation of virtually all coreference resolution models is the focus on single-antecedent anaphors.
Improving Robustness by Augmenting Training Sentences with Predicate-Argument Structures
Existing approaches to improve robustness against dataset biases mostly focus on changing the training objective so that models learn less from biased examples.
Improving QA Generalization by Concurrent Modeling of Multiple Biases
Our framework weights each example based on the biases it contains and the strength of those biases in the training data.
Towards Debiasing NLU Models from Unknown Biases
Recently proposed debiasing methods are shown to be effective in mitigating this tendency.
Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance
Models for natural language understanding (NLU) tasks often rely on the idiosyncratic biases of the dataset, which make them brittle against test cases outside the training distribution.
Neural Duplicate Question Detection without Labeled Training Data
We show that our proposed approaches are more effective in many cases because they can utilize larger amounts of unlabeled data from cQA forums.
Improving Generalization by Incorporating Coverage in Natural Language Inference
Finally, we show that using the coverage information is not only beneficial for improving the performance across different datasets of the same task.
Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection
To address this problem, minimum spans are manually annotated in smaller corpora.
Using Linguistic Features to Improve the Generalization Capability of Neural Coreference Resolvers
We show that generalization improves only slightly by merely using a set of additional linguistic features.
Revisiting Selectional Preferences for Coreference Resolution
Selectional preferences have long been claimed to be essential for coreference resolution.
Lexical Features in Coreference Resolution: To be Used With Caution
Lexical features are a major source of information in state-of-the-art coreference resolvers.
Use Generalized Representations, But Do Not Forget Surface Features
Only a year ago, all state-of-the-art coreference resolvers were using an extensive amount of surface features.
