@conference {5302,
	title = {The Aligned Multimodal Movie Treebank: An audio, video, dependency-parse treebank},
	booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
	year = {2022},
	abstract = {<p>Treebanks have traditionally included only text and were derived from written sources such as newspapers or the web. We introduce the Aligned Multimodal Movie Treebank (AMMT), an English language treebank derived from dialog in Hollywood movies which includes transcriptions of the audio-visual streams with word-level alignment, as well as part of speech tags and dependency parses in the Universal Dependencies formalism. AMMT consists of 31,264 sentences and 218,090 words, that will amount to the 3rd largest UD English treebank and the only multimodal treebank in UD. To help with the web-based annotation effort, we also introduce the Efficient Audio Alignment Annotator (EAAA), a companion tool that enables annotators to significantly speed-up their annotation processes.</p>
},
	author = {Adam Yaari and Jan DeWitt and Henry Hu and Bennett Stankovits and Sue Felshin and Yevgeni Berzak and Helena Aparicio and Boris Katz and Ignacio Cases and Andrei Barbu}
}
