The United Nations Development Programme (UNDP) helps countries implement the United Nations (UN) Sustainable Development Goals (SDGs), an agenda for tackling major societal issues such as poverty, hunger, and environmental degradation by the year 2030. A key service provided by UNDP to countries that seek it is a review of national development plans and sector strategies by policy experts to assess alignment of national targets with one or more of the 169 targets of the 17 SDGs. Known as the Rapid Integrated Assessment (RIA), this process involves manual review of hundreds, if not thousands, of pages of documents and takes weeks to complete. In this work, we develop a natural language processing-based methodology to accelerate the workflow of policy experts. Specifically we use paragraph embedding techniques to find paragraphs in the documents that match the semantic concepts of each of the SDG targets. One novel technical contribution of our work is in our use of historical RIAs from other countries as a form of neighborhood-based supervision for matches in the country under study. We have successfully piloted the algorithm to perform the RIA for Papua New Guinea’s national plan, with the UNDP estimating it will help reduce their completion time from an estimated 3-4 weeks to 3 days.

We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to state-of-the-art. Our model has the additional advantage of being very interpretable, since it allows visualization of its predictions broken up by abstract features such as information content, salience and novelty. Another novel contribution of our work is abstractive training of our extractive model that can train on human generated reference summaries alone, eliminating the need for sentence-level extractive labels.

Many natural language understanding (NLU) tasks, such as shallow parsing (i.e., text chunking) and semantic slot filling, require the assignment of representative labels to the meaningful chunks in a sentence. Most of the current deep neural network (DNN) based methods consider these tasks as a sequence labeling problem, in which a word, rather than a chunk, is treated as the basic unit for labeling. These chunks are then inferred by the standard IOB (Inside-Outside- Beginning) labels. In this paper, we propose an alternative approach by investigating the use of DNN for sequence chunking, and propose three neural models so that each chunk can be treated as a complete unit for labeling. Experimental results show that the proposed neural sequence chunking models can achieve start-of-the-art performance on both the text chunking and slot filling tasks.