Secondly, integrating multiword expressions in the parser grammar followed by a reranker specific to such expressions slightly improves all evaluation metrics.

Our proposal is to evaluate two discriminative strategies in a real constituency parsing context: (a) pre-grouping MWE before parsing; this would be done with a state-of-the-art recognizer based on Conditional Random Fields; (b) parsing with a grammar including MWE identification and then reranking the output parses thanks to a Maximum Entropy model integrating MWE-dedicated features.

In order to make these models comparable, we use two comparable sets of feature templates: one adapted to sequence labelling (CRF—based MWER) and the other one adapted to reranking (MaXEnt-based reranker ).

The reranker templates are instantiated only for the nodes of the candidate parse tree, which are leaves dominated by a MWE node (i.e.

o RERANKER : for each leaf (in position 77.)

3.2 Reranking

Discriminative reranking consists in reranking the n-best parses of a baseline parser with a discriminative model, hence integrating features associated with each node of the candidate parses.

Formally, given a sentence 8, the reranker selects the best candidate parse p among a set of candidates P (s) with respect to a scoring function V9:

The hypergraph structure encodes exponentially many derivations, which we rerank discriminatively using local and global features.

ia Discriminative Reranking

The performance of this baseline system could be potentially further improved using discriminative reranking (Collins, 2000).

Typically, this method first creates a list of n-best candidates from a generative model, and then reranks them with arbitrary features (both local and global) that are either not computable or intractable to compute within the

The hypergraph representation allows us to decompose the feature functions and compute them piecemeal at each hyperarc (or sub-derivation), rather than at the root node as in conventional n-best list reranking .

Discriminative reranking has been employed in many NLP tasks such as syntactic parsing (Char-niak and Johnson, 2005; Huang, 2008), machine translation (Shen et al., 2004; Li and Khudanpur, 2009) and semantic parsing (Ge and Mooney, 2006).

Our model is closest to Huang (2008) who also performs forest reranking on a hypergraph, using both local and nonlocal features, whose weights are tuned with the averaged perceptron algorithm (Collins, 2002).

We adapt forest reranking to generation and introduce several task-specific features that boost performance.

Our SR-TSG parser achieves an F 1 score of 92.4% in the Wall Street Journal (WSJ) English Penn Treebank parsing task, which is a 7.7 point improvement over a conventional Bayesian TSG parser, and better than state-of-the-art discriminative reranking parsers.

It should be noted that discriminative reranking parsers such as (Char-niak and Johnson, 2005) and (Huang, 2008) are constructed on a generative parser.

The reranking parser takes the k-best lists of candidate trees or a packed forest produced by a baseline parser (usually a generative model), and then reranks the candidates using arbitrary features.

Hence, we can expect that combining our SR-TSG model with a discriminative reranking parser would provide better performance than SR-TSG alone.

Our SR-TSG parser achieves an F1 score of 92.4% in the WSJ English Penn Treebank parsing task, which is a 7.7 point improvement over a conventional Bayesian TSG parser, and superior to state-of-the-art discriminative reranking parsers.

We report machine translation reranking results in Section 5.4.

The latter report results for two binary classifiers: RERANK uses the reranking features of Charniak and Johnson (2005), and TSG uses

All generative models improve, but TREELET-RULE remains the best, now outperforming the RERANK system, though of course it is likely that RERANK would improve if it could be scaled up to more training data.

We also show fluency improvements in a preliminary machine translation reranking experiment.