In particular, we extend the monolingual infinite tree model (Finkel et al., 2007) to a bilingual scenario: each hidden state (POS tag) of a source-side dependency tree emits a source word together with its aligned target word, either jointly ( joint model ), or independently (independent model).

Our independent model gains over 1 point in BLEU by resolving the sparseness problem introduced in the joint model .

This paper proposes two types of models that differ in their processes for generating observations: the joint model and the independent model.

3.1 Joint Model

The joint model is a simple application of the infinite tree model under a bilingual scenario.

We investigate two types of models: (i) a joint model and (ii) an independent model.

In the joint model , each hidden state jointly emits both a source word and its aligned target word as an observation.

Figure 4: An Example of the Joint Model

This paper introduces a graph-based semi-supervised joint model of Chinese word segmentation and part-of-speech tagging.

An inductive character-based joint model is obtained eventually.

In the past years, several proposed supervised joint models (Ng and Low, 2004; Zhang and Clark, 2008; Jiang et al., 2009; Zhang and Clark, 2010) achieved reasonably accurate results, but the outstanding problem among these models is that they rely heavily on a large amount of labeled data, i.e., segmented texts with POS tags.

It is directed to maximize the conditional likelihood of hidden states with the derived label distributions on unlabeled data, i.e., p(y, vlzc), where y and v are jointly modeled but

Firstly, as expected, for the two supervised baselines, the joint model outperforms the pipeline one, especially on segmentation.

This outcome verifies the commonly accepted fact that the joint model can substantially improve the pipeline one, since POS tags provide additional information to word segmentation (Ng and Low, 2004).

The state-of-the-art joint models include reranking approaches (Shi and Wang, 2007), hybrid approaches (Nakagawa and Uchimoto, 2007; Jiang et al., 2008; Sun, 2011), and single-model approaches (Ng and Low, 2004; Zhang and Clark, 2008; Kruengkrai et al., 2009; Zhang and Clark, 2010).

We extend a nonparametric model of word segmentation by adding phonological rules that map from underlying forms to surface forms to produce a mathematically well-defined joint model as a first step towards handling variation and segmentation in a single model.

We analyse how our model handles /t/-deletion on a large corpus of transcribed speech, and show that the joint model can perform word segmentation and recover underlying /t/s.

However, as they point out, combining the segmentation and the variation model into one joint model is not straightforward and usual inference procedures are infeasible, which requires the use of several heuristics.

They do not aim for a joint model that also handles word segmentation, however, and rather than training their model on an actual corpus, they evaluate on constructed lists of examples, mimicking frequencies of real data.

We presented a joint model for word segmentation and the learning of phonological rule probabilities from a corpus of transcribed speech.

Figure l: The graphical model for our joint model of word-final /t/-deletion and Bigram word segmentation.

(2009) segmentation models, exact inference is infeasible for our joint model .

Joint models of sentence extraction and compression have a great benefit in that they have a large degree of freedom as far as controlling redundancy goes.

In contrast, conventional two-stage approaches (Za-jic et al., 2006), which first generate candidate compressed sentences and then use them to generate a summary, have less computational complexity than joint models .

Joint models can prune unimportant or redundant descriptions without resorting to enumeration.

Therefore, the joint model can extract an arbitrarily compressed sentence as a subtree without enumerating all candidates.

The joint model can remove the redundant part as well as the irrelevant part of a sentence, because the model simultaneously extracts and compresses sentences.

In this joint model , we generate a compressed sentence by extracting an arbitrary subtree from a dependency tree of a sentence.

A natural extension of our unified framework is to construct a joint model in which the predictions for all three tasks inform each other at all stages of the prediction process.

(2012) presented a joint model for inducing simple syntactic frames and VCs.

(2012) introduced a joint model for SCF and SP acquisition.

Joint Modeling A small number of works have recently investigated joint approaches to SCFs, SPs and VCs.

Although evaluation of these recent joint models has been partial, the results have been encouraging and fur-

DPPs are particularly suitable for joint modeling as they come with various simple and intuitive ways to combine individual model kernel matrices into a joint kernel.

In this paper, we propose Two-Neighbor Orientation (TNO) model that jointly models the orientation decisions between anchors and two neighboring multi-unit chunks which may cross phrase or rule boundaries.

Our approach, which we formulate as a Two-Neighbor Orientation model, includes the joint modeling of two orientation decisions and the modeling of the maximal span of the reordered chunks through the concept of Maximal Orientation Span.

Then, we jointly model the orientations of chunks that immediately precede and follow the anchors (hence, the name “two-neighbor”) along with the maximal span of these chunks, to which we refer as Maximal Orientation Span (MOS).

To show the effectiveness of our model, we integrate our TNO model into a state-of-the-art syntax-based SMT system, which uses synchronous context-free grammar (SCFG) rules to jointly model reordering and lexical translation.

Our Two-Neighbor Orientation model (TNO) designates A C A(@) as anchors and jointly models the orientation of chunks that appear immediately to the left and to the right of the anchors as well as the identities of these chunks.

We propose a novel joint event extraction algorithm to predict the triggers and arguments simultaneously, and use the structured perceptron (Collins, 2002) to train the joint model .

Unfortunately, it is intractable to perform the exact search in our framework because: (1) by jointly modeling the trigger labeling and argument labeling, the search space becomes much more complex.

To the best of our knowledge, our work is the first attempt to jointly model these two ACE event subtasks.

There has been some previous work on joint modeling for biomedical events (Riedel and McCallum, 2011a; Riedel et al., 2009; McClosky et al., 2011; Riedel and McCallum, 2011b).

We can see that both character-level joint models outperform the pipelined system; our model with annotated word structures gives an improvement of 0.97% in tagging accuracy and 2.17% in phrase-structure parsing accuracy.

The results also demonstrate that the annotated word structures are highly effective for syntactic parsing, giving an absolute improvement of 0.82% in phrase-structure parsing accuracy over the joint model with flat word structures.

(2011), which additionally uses the Chinese Gigaword Corpus; Li ’11 denotes a generative model that can perform word segmentation, POS tagging and phrase-structure parsing jointly (Li, 2011); Li+ ’12 denotes a unified dependency parsing model that can perform joint word segmentation, POS tagging and dependency parsing (Li and Zhou, 2012); Li ’11 and Li+ ’12 exploited annotated morphological-level word structures for Chinese; Hatori+ ’12 denotes an incremental joint model for word segmentation, POS tagging and dependency parsing (Hatori et al., 2012); they use external dictionary resources including HowNet Word List and page names from the Chinese Wikipedia; Qian+ ’12 denotes a joint segmentation, POS tagging and parsing system using a unified framework for decoding, incorporating a word segmentation model, a POS tagging model and a phrase-structure parsing model together (Qian and Liu, 2012); their word segmentation model is a combination of character-based model and word-based model.

Their work demonstrates that a joint model can improve the performance of the three tasks, particularly for POS tagging and dependency parsing.

In Table 2, we report on the results of the best single models and the best joint model .

For the joint model , we employed the best single PPR configuration, and a COS configuration that uses sense gloss extended by Wiktionary hypernyms, synonyms and FrameNet frame name and frame definition, to achieve the highest score, an F1-score of 0.739.

The BEST JOINT model performs well on nouns, slightly better on adjectives, and worse on verbs, see Table 2.