Abstract: We describe an effort to annotate a corpus of natural language instructions consisting of 622 wet lab protocols to facilitate automatic or semi-automatic conversion of protocols into a machine-readable format and benefit biological research. Experimental results demonstrate the utility of our corpus for developing machine learning approaches to shallow semantic parsing of instructional texts. We make our annotated Wet Lab Protocol Corpus available to the research community.

Introduction: As the complexity of biological experiments increases, there is a growing need to automate wet laboratory procedures to avoid mistakes due to human error and also to enhance the reproducibility of experimental biological research BIBREF0 . Several efforts are currently underway to define machine-readable formats for writing wet lab protocols BIBREF1 , BIBREF2 , BIBREF3 . The vast majority of today's protocols, however, are written in natural language with jargon and colloquial language constructs that emerge as a byproduct of ad-hoc protocol documentation. This motivates the need for machine reading systems that can interpret the meaning of these natural language instructions, to enhance reproducibility via semantic protocols (e.g. the Aquarium project) and enable robotic automation BIBREF4 by mapping natural language instructions to executable actions. In this study we take a first step towards this goal by annotating a database of wet lab protocols with semantic actions and their arguments; and conducting initial experiments to demonstrate its utility for machine learning approaches to shallow semantic parsing of natural language instructions. To the best of our knowledge, this is the first annotated corpus of natural language instructions in the biomedical domain that is large enough to enable machine learning approaches. There have been many recent data collection and annotation efforts that have initiated natural language processing research in new directions, for example political framing BIBREF5 , question answering BIBREF6 and cooking recipes BIBREF7 . Although mapping natural language instructions to machine readable representations is an important direction with many practical applications, we believe current research in this area is hampered by the lack of available annotated corpora. Our annotated corpus of wet lab protocols could enable further research on interpreting natural language instructions, with practical applications in biology and life sciences. Prior work has explored the problem of learning to map natural language instructions to actions, often learning through indirect supervision to address the lack of labeled data in instructional domains. This is done, for example, by interacting with the environment BIBREF8 , BIBREF9 or observing weakly aligned sequences of instructions and corresponding actions BIBREF10 , BIBREF11 . In contrast, we present the first steps towards a pragmatic approach based on linguistic annotation (Figure FIGREF4 ). We describe our effort to exhaustively annotate wet lab protocols with actions corresponding to lab procedures and their attributes including materials, instruments and devices used to perform specific actions. As we demonstrate in § SECREF6 , our corpus can be used to train machine learning models which are capable of automatically annotating lab-protocols with action predicates and their arguments BIBREF12 , BIBREF13 ; this could provide a useful linguistic representation for robotic automation BIBREF14 and other downstream applications.

Question: what ML approaches did they experiment with?
Answer: MaxEnt, BiLSTM, BiLSTM+CRF