Allen Institute for AI

Missing Elements

Missing Elements

Missing Elements

Current natural language processing technology aims to process what is explicitly mentioned in text. But what about the elements that are being left out of the text, yet are easily and naturally inferred by the human hearer? Can our computer programs identify and infer such elements too? In this project, we develop benchmarks and models to endow NLP applications with this capacity.
About Missing Elements

Empty elements are elements left out by the speaker and inferred by the hearer. They are central to obtaining human-like language understanding.
Some examples of empty elements are:
• Fused heads: I bought three apples and ate two [apples].
• Verb-phrase ellipsis: John bought books, Jane didn’t [buy books].
• Aspectual verb constructions: I finished [reading? writing?] the book.
• Bridging: I love these shoes. The color [of the shoes] is beautiful!

In this project, we investigate Empty Elements Expansion (EEE), a challenge wherein we aim to automatically recognize and resolve such empty elements in texts.

Bridging, for example, is a (non-identity) implicit relation between two nouns in the text. For example, in the sentence “I entered the room. The ceiling was high” there is an implicit part-whole relation between “the ceiling” and “the room”, yielding the “complete” expression “the ceiling [of the room]”. How can we collect data concerning those missing elements? How can we design learning models to infer them? How can these empty elements aid Natural Language Understanding applications, and in particular, Natural Language Programming?

For more detail about bridging and the data collection interface, check out our introduction and qualification tests and our annotation tool.


AI2 Israel Members

  • Reut Tsarfaty's Profile PhotoReut TsarfatyResearch
  • Yoav Goldberg's Profile PhotoYoav GoldbergResearch Director, AI2 Israel


  • Victoria Basmova's Profile PhotoVictoria BasmovaIntern
  • Yanai Elazar's Profile PhotoYanai ElazarIntern