Text-editing models have recently become a prominent alternative to seq2seq models for monolingual text-generation tasks such as grammatical error correction, simplification, and style transfer.
This paper presents a simple recipe to train state-of-the-art multilingual Grammatical Error Correction (GEC) models.
Ranked #1 on Grammatical Error Correction on Falko-MERLIN (using extra training data)
We propose LaserTagger - a sequence tagging approach that casts text generation as a text editing task.
Ranked #1 on Sentence Fusion on DiscoFuse
Conversational agents offer users a natural-language interface to accomplish tasks, entertain themselves, or access information.
A common approach is to exploit existing knowledge to learn linguistic patterns expressing the target relation and use these patterns for extracting new relation mentions.
We formulate the task of discourse connective prediction and release a dataset of 2. 9M sentence pairs separated by discourse connectives for this task.
In relation extraction, a key process is to obtain good detectors that find relevant sentences describing the target relation.
Recent research shows the importance of linking linguistic knowledge resources for the creation of large-scale linguistic data.
Some express a relation that entails the target relation.
It will be shown how segmented Hebrew language data can be granularly described in a Linked Data format, thus, serving as an exemplary case for creating morpheme inventories of any inflectional language with MMoOn.
The current corpus is already in active use in our research for evaluation of the relation extraction performance of our automatically learned extraction patterns.
In this paper, we present a novel combination of two types of language resources dedicated to the detection of relevant relations (RE) such as events or facts across sentence boundaries.