One of the main obstacles to producing high quality joint models is the lack of jointly annotated data.

Joint modeling of multiple natural language processing tasks outperforms single-task models learned from the same data, but still under-performs compared to single-task models learned on the more abundant quantities of available single-task annotated data.

In this paper we present a novel model which makes use of additional single-task annotated data to improve the performance of a joint model .

Joint models can be particularly useful for producing analyses of sentences which are used as input for higher-level, more semantically-oriented systems, such as question answering and machine translation.

However, designing joint models which actually improve performance has proven challenging.

There have been some recent successes with joint modeling .

3.2 A Joint Model for Argument Classification

It incorporates strong dependencies within a comprehensive statistical joint model with a rich set of features over multiple argument phrases.

First local models are applied to produce role labels over individual arguments, then the joint model is used to decide the entire argument sequence among the set of the n-best competing solutions.

It also focuses on jointly modeling the generation of both predicate and argument, and evaluation is performed on a set of human-plausibility judgments obtaining impressive results against Keller and Lapata’s (2003) Web hit-count based system.

One weakness of IndependentLDA is that it doesn’t jointly model a1 and a2 together.

On the one hand, J ointLDA jointly models the generation of both arguments in an extracted tuple.