This paper proposes a Markov Decision Process and reinforcement learning based approach for domain selection in a multidomain Spoken Dialogue System built on a distributed architecture .

In the proposed framework , the domain selection problem is treated as sequential planning instead of classification , such that confirmation and clarification interaction mechanisms are supported .

In addition , it is shown that by using a model parameter tying trick , the extensibility of the system can be preserved , where dialogue components in new domains can be easily plugged in , without re-training the domain selection policy .

The experimental results based on human subjects suggest that the proposed model marginally outperforms a non-trivial baseline .

Due to growing demand for natural humanmachine interaction , over the last decade Spoken Dialogue Systems ( SDS ) have been increasingly deployed in various commercial applications ranging from traditional call centre automation ( e.g. AT&T " Lets Go ! "

bus information system ( Williams et al . , 2010 ) ) to mobile personal assistants and knowledge navigators ( e.g. Apple 's Siri R , Google Now TM , Microsoft Cortana , etc . ) or voice interaction for smart household appliance control ( e.g. Samsung Evolution Kit for Smart TVs ) .

Furthermore , latest progress in openvocabulary Automatic Speech Recognition ( ASR ) is pushing SDS from traditional single-domain information systems towards more complex multidomain speech applications , of which typical examples are those voice assistant mobile applications .

Recent advances in SDS have shown that statistical approaches to dialogue management can result in marginal improvement in both the naturalness and the task success rate for domainspecific dialogues ( Lemon and Pietquin , 2012 ; Young et al . , 2013 ) .

State-of-the-art statistical SDS treat the dialogue problem as a sequential decision making process , and employ established planning models , such as Markov Decision Processes ( MDPs ) ( Singh et al . , 2002 ) or Partially Observable Markov Decision Processes ( POMDPs ) ( Thomson and Young , 2010 ; Young et al . , 2010 ; Williams and Young , 2007 ) , in conjunction with reinforcement learning techniques ( Jurčíček et al . , 2011 ; Jurčíček et al . , 2012 ; Gašić et al . , 2013a ) to seek optimal dialogue policies that maximise long-term expected ( discounted ) rewards and are robust to ASR errors .

However , to the best of our knowledge , most of the existing multi-domain SDS in public use are rule-based ( e.g. ( Gruber et al . , 2012 ; Mirkovic and Cavedon , 2006 ) ) .

The application of statistical models in multi-domain dialogue systems is still preliminary .

Komatani et al . ( 2006 ) and Nakano et al . ( 2011 ) utilised a distributed architecture ( Lin et al . , 1999 ) to integrate expert dialogue systems in different domains into a unified framework , where a central controller trained as a data-driven classifier selects a domain expert at each turn to address user 's query .

Alternatively , Hakkani-Tür et al . ( 2012 ) adopted the well-known Information State mechanism ( Traum and Larsson , 2003 ) to construct a multi-domain SDS and proposed a discriminative classification model for more accurate state updates .

More recently , Gašić et al . ( 2013b ) proposed that by a simple expansion of the kernel function in Gaussian Process ( GP ) reinforcement learning ( Engel et al . , 2005 ; Gašić et al . , 2013a ) , one can adapt pre-trained dialogue policies to handle unseen slots for SDS in extended domains .

In this paper , we use a voice assistant applica-

Figure 1 : The distributed architecture of the voice assistant system ( a simplified illustration ) .

tion ( similar to Apple 's Siri but in Chinese language ) as an example to demonstrate a novel MDP-based approach for central interaction management in a complex multi-domain dialogue system .

The voice assistant employs a distributed architecture similar to ( Lin et al . , 1999 ; Komatani et al . , 2006 ; Nakano et al . , 2011 ) , and handles mixed interactions of multi-turn dialogues across different domains and single-turn queries powered by a collection of information access services ( such as web search , Question Answering ( QA ) , etc . ) .

In our system , the dialogues in each domain are managed by an individual domain expert SDS , and the single-turn services are used to handle those so-called out-of-domain requests .

We use featurised representations to summarise the current dialogue states in each domain ( see Section 3 for more details ) , and let the central controller ( the MDP model ) choose one of the following system actions at each turn : ( 1 ) addressing user 's query based on a domain expert , ( 2 ) treating it as an out-of-domain request , ( 3 ) asking user to confirm whether he / she wants to continue a domain expert 's dialogue or to switch to out-of-domain services , and ( 4 ) clarifying user 's intention between two domains .

The Gaussian Process Temporal Difference ( GPTD ) algorithm ( Engel et al . , 2005 ; Gašić et al . , 2013a ) is adopted here for policy optimisation based on human subjects , where a parameter tying trick is applied to preserve the extensibility of the system , such that new domain experts ( dialogue systems ) can be flexibly plugged in without the need of re-training the central controller .

Comparing to the previous classification-based methods ( Komatani et al . , 2006 ; Nakano et al . , 2011 ) , the proposed approach not only has the advantage of action selection in consideration of long-term rewards , it can also yield more robust policies that allow clarifications and confirmations to mitigate ASR and Spoken Language Understanding ( SLU ) errors .

Our human evaluation results show that the proposed system with a trained MDP policy achieves significantly better naturalness in domain switching tasks than a non-trivial baseline with a hand-crafted policy .

The remainder of this paper is organised as follows .

Section 2 defines the terminology used throughout the paper .

Section 3 briefly overviews the distributed architecture of our system .

The MDP model and the policy optimisation algorithm are introduced in Section 4 and Section 5 , respectively .

After this , experimental settings and evaluation results are described in Section 6 .

Finally , we discuss some possible improvements in Section 7 and conclude ourselves in Section 8 .

In this paper , we introduce an MDP framework for learning domain selection policies in a complex multi-domain SDS .

Standard problem formulation is modified with tied model parameters , so that the entire system is extensible and new domain experts can be easily integrated without re-training the policy .

This expectation is confirmed by empirical experiments with human subjects , where the proposed system marginally beats a non-trivial baseline and demonstrates proper extensibility .

Several possible improvements are discussed , which will be the central arc of our future research .

Thomas Robert Gruber , Adam John Cheyer , Dag


In this paper , we address a unique problem in Chinese language processing and report on our study on extending a Chinese thesaurus with region-specific words , mostly from the financial domain , from various Chinese speech communities .

With the larger goal of automatically constructing a Pan-Chinese lexical resource , this work aims at taking an existing semantic classificatory structure as leverage and incorporating new words into it .

In particular , it is important to see if the classification could accommodate new words from heterogeneous data sources , and whether simple similarity measures and clustering methods could cope with such variation .

We use the cosine function for similarity and test it on automatically classifying 120 target words from four regions , using different datasets for the extraction of feature vectors .

The automatic classification results were evaluated against human judgement , and the performance was encouraging , with accuracy reaching over 85 % in some cases .

Thus while human judgement is not straightforward and it is difficult to create a Pan-Chinese lexicon manually , it is observed that combining simple clustering methods with the appropriate data sources appears to be a promising approach toward its automatic construction .

Large-scale semantic lexicons are important resources for many natural language processing ( NLP ) tasks .

For a significant world language such as Chinese , it is especially critical to capture the substantial regional variation as an important part of the lexical knowledge , which will be useful for many NLP applications , including natural language understanding , information retrieval , and machine translation .

Existing Chinese lexical resources , however , are often based on language use in one particular region and thus lack the desired comprehensiveness .

Toward this end , Tsou and Kwong ( 2006 ) proposed a comprehensive Pan-Chinese lexical resource , based on a large and unique synchronous Chinese corpus as an authentic source for lexical acquisition and analysis across various Chinese speech communities .

To allow maximum versatility and portability , it is expected to document the core and universal substances of the language on the one hand , and also the more subtle variations found in different communities on the other .

Different Chinese speech communities might share lexical items in the same form but with different meanings .

For instance , the word 居屋 refers to general housing in Mainland China but specifically to housing under the Home Ownership Scheme in Hong Kong ; and while the word 住房 is similar to 居屋 to mean general housing in Mainland China , it is rarely seen in the Hong Kong context .

Hence , the current study aims at taking an existing Chinese thesaurus , namely the Tongyici Cilin 同義詞詞林 , as leverage and extending it with lexical items specific to individual Chinese speech communities .

In particular , the feasibility depends on the following issues : ( 1 ) Can lexical items from various Chinese speech communities , that is , from such heterogeneous sources , be classified as effectively with methods shown to work for clustering closely related words from presumably the same , or homogenous , source ?

( 2 ) Could existing semantic classificatory structures accommodate concepts and expressions specific to individual Chinese speech communities ?

Measuring similarity will make sense only if the feature vectors of the two words under comparison are directly comparable .

There is usually no problem if both words and their contextual features are from the same data source .

Since Tongyici Cilin ( or simply Cilin hereafter ) is based on the vocabulary used in Mainland China , it is not clear how often these words will be found in data from other places , and even if they are found , how well the feature vectors extracted could reflect the expected usage or sense .

Our hypothesis is that it will be more effective to classify new words from Mainland China with respect to Cilin categories , than to do the same on new words from regions outside Mainland China .

Furthermore , if this hypothesis holds , one would need to consider separate mechanisms to cluster heterogeneous regionspecific words in the Pan-Chinese context .

Thus in the current study we sampled 30 target words specific to each of Beijing , Hong Kong , Singapore , and Taipei , from the financial domain ; and used the cosine similarity function to classify them into one or more of the semantic categories in Cilin .

The automatic classification results were compared with a simple baseline method , against human judgement as the gold standard .

In general , an accuracy of up to 85 % could be reached with the top 15 candidates considered .

It turns out that our hypothesis is supported by the Taipei test data , whereas the data heterogeneity effect is less obvious in Hong Kong and Singapore test data , though the effect on individual test items varies .

In Section 2 , we will briefly review related work and highlight the innovations of the current study .

In Sections 3 and 4 , we will describe the materials used and the experimental setup respectively .

Results will be presented and discussed with future directions in Section 5 , followed by a conclusion in Section 6 .

In this paper , we have reported our study on a unique problem in Chinese language processing , namely extending a Chinese thesaurus with new words from various Chinese speech communities , including Beijing , Hong Kong , Singapore and Taipei .

The critical issues include whether the existing classificatory structure could accommodate concepts and expressions specific to various Chinese speech communities , and whether the difference in textual sources might pose difficulty in using conventional similarity measures for the automatic classification .

Our experiments , using the cosine function to measure similarity and testing with various sources for extracting contextual vectors , suggest that the classification performance might depend on the compatibility between the words in the thesaurus and the sources from which the target words are drawn .

Evaluated against human judgement , an accuracy of over 85 % was reached in some cases , which were much higher than the baseline and were very encouraging in general .

While human judgement is not straightforward and it is difficult to create a Pan-Chinese lexicon manually , combining simple classification methods with the appropriate data sources seems to be a promising approach toward its automatic construction .


This paper presents a domain-assisted approach to organize various aspects of a product into a hierarchy by integrating domain knowledge ( e.g. , the product specifications ) , as well as consumer reviews .

Based on the derived hierarchy , we generate a hierarchical organization of consumer reviews on various product aspects and aggregate consumer opinions on these aspects .

With such organization , user can easily grasp the overview of consumer reviews .

Furthermore , we apply the hierarchy to the task of implicit aspect identification which aims to infer implicit aspects of the reviews that do not explicitly express those aspects but actually comment on them .

The experimental results on 11 popular products in four domains demonstrate the effectiveness of our approach .

With the rapidly expanding e-commerce , most retail Web sites encourage consumers to write reviews to express their opinions on various aspects of products .

Huge collections of consumer reviews are now available on the Web .

These reviews have become an important resource for both consumers and firms .

Consumers commonly seek quality information from online consumer reviews prior to purchasing a product , while many firms use online reviews as an important resource in their product development , marketing , and consumer relationship management .

However , the reviews are disorganized , leading to the difficulty in information navigation and knowledge acquisition .

It is impractical for user to grasp the overview of consumer reviews and opinions on various aspects of a product from such enormous reviews .

Among hundreds of product aspects , it is also inefficient for user to browse consumer reviews and opinions on a specific aspect .

Thus , there is a compelling need to organize consumer reviews , so as to transform the reviews into a useful knowledge structure .

Since the hierarchy can improve information representation and accessibility ( Cimiano , 2006 ) , we propose to organize the aspects of a product into a hierarchy and generate a hierarchical organization of consumer reviews accordingly .

Towards automatically deriving an aspect hierarchy from the reviews , we could refer to traditional hierarchy generation methods in ontology learning , which first identify concepts from the text , then determine the parent-child relations between these concepts using either pattern-based or clusteringbased methods ( Murthy et al . , 2010 ) .

However , pattern-based methods usually suffer from inconsistency of parent-child relationships among the concepts , while clustering-based methods often result in low accuracy .

Thus , by directly utilizing these methods to generate an aspect hierarchy from consumer reviews , the resulting hierarchy is usually inaccurate , leading to unsatisfactory review organization .

On the other hand , domain knowledge of products is now available on the Web .

For example , there are more than 248,474 product specifications in the product selling Web site CNet.com ( Beckham , 2005 ) .

These product specifications cover some product aspects and provide coarse-grained parentchild relations among these aspects .

Such domain knowledge is useful to help organize the product as-140 pects into a hierarchy .

However , the initial hierarchy obtained from domain knowledge usually can not fit the review data well .

For example , the initial hierarchy is usually too coarse and may not cover the specific aspects commented in the reviews , while some aspects in the hierarchy may not be of interests to users in the reviews .

Motivated by the above observations , we propose in this paper to organize the product aspects into a hierarchy by simultaneously exploiting the domain knowledge ( e.g. , the product specification ) and consumer reviews .

With derived aspect hierarchy , we generate a hierarchical organization of consumer reviews on various aspects and aggregate consumer opinions on these aspects .

Figure 1 illustrates a sample of hierarchical review organization for the product " iPhone 3 G " .

With such organization , users can easily grasp the overview of product aspects as well as conveniently navigate the consumer reviews and opinions on any aspect .

For example , users can find that 623 reviews , out of 9,245 reviews , are about the aspect " price " , with 241 positive and 382 negative reviews .

Given a collection of consumer reviews on a specific product , we first automatically acquire an initial aspect hierarchy from domain knowledge and identify the aspects from the reviews .

Based on the initial hierarchy , we develop a multi-criteria optimization approach to construct an aspect hierarchy to contain all the identified aspects .

Our approach incrementally inserts the aspects into the initial hierarchy based on inter-aspect semantic distance , a metric used to measure the semantic relation among aspects .

In order to derive reliable semantic distance , we propose to leverage external hierarchies , sampled from WordNet and Open Directory Project , to assist semantic distance learning .

With resultant aspect hierarchy , the consumer reviews are then organized to their corresponding aspect nodes in the hierarchy .

We then perform sentiment classification to determine consumer opinions on these aspects .

Furthermore , we apply the hierarchy to the task of implicit aspect identification .

This task aims to infer implicit aspects of the reviews that do not explicitly express those aspects but actually comment on them .

For example , the implicit aspect of the review " It is so expensive " is " price . "

Most existing aspect identification approaches rely on the appearance of aspect terms , and thus are not able to handle implicit aspect problem .

Based on our aspect hierarchy , we can infer the implicit aspects by clustering the reviews into their corresponding aspect nodes in the hierarchy .

We conduct experiments on 11 popular products in four domains .

More details of the corpus are discussed in Section 4 .

The experimental results demonstrate the effectiveness of our approach .

The main contributions of this work can be summarized as follows :

1 ) We propose to hierarchically organize consumer reviews according to an aspect hierarchy , so as to transfer the reviews into a useful knowledge structure .

2 ) We develop a domain-assisted approach to generate an aspect hierarchy by integrating domain knowledge and consumer reviews .

In order to derive reliable semantic distance between aspects , we propose to leverage external hierarchies to assist semantic distance learning .

3 ) We apply the aspect hierarchy to the task of implicit aspect identification , and achieve satisfactory performance .

The rest of this paper is organized as follows .

Our approach is elaborated in Section 2 and applied to implicit aspect identification in Section 3 .

Section 4 presents the evaluations , while Section 5 reviews 141 related work .

Finally , Section 6 concludes this paper with future works .

In this paper , we have developed a domain-assisted approach to generate product aspect hierarchy by integrating domain knowledge and consumer reviews .

Based on the derived hierarchy , we can generate a hierarchical organization of consumer reviews as well as consumer opinions on the aspects .

With such organization , user can easily grasp the overview of consumer reviews , as well as seek consumer reviews and opinions on any specific aspect by navigating through the hierarchy .

We have further applied the hierarchy to the task of implicit aspect identification .

We have conducted evaluations on 11 different products in four domains .

The experimental results have demonstrated the effectiveness of our approach .

In the future , we will explore other linguistic features to learn the semantic distance between aspects , as well as apply our approach to other applications .

With such organization , user can easily grasp the overview of consumer reviews .

We conduct experiments on 11 popular products in four domains .

The experimental results demonstrate the effectiveness of our approach .

We have conducted evaluations on 11 different products in four domains .

The experimental results have demonstrated the effectiveness of our approach .


We present a novel technique for learning semantic representations , which extends the distributional hypothesis to multilingual data and joint-space embeddings .

Our models leverage parallel data and learn to strongly align the embeddings of semantically equivalent sentences , while maintaining sufficient distance between those of dissimilar sentences .

The models do not rely on word alignments or any syntactic information and are successfully applied to a number of diverse languages .

We extend our approach to learn semantic representations at the document level , too .

We evaluate these models on two cross-lingual document classification tasks , outperforming the prior state of the art .

Through qualitative analysis and the study of pivoting effects we demonstrate that our representations are semantically plausible and can capture semantic relationships across languages without parallel data .

Distributed representations of words provide the basis for many state-of-the-art approaches to various problems in natural language processing today .

Such word embeddings are naturally richer representations than those of symbolic or discrete models , and have been shown to be able to capture both syntactic and semantic information .

Successful applications of such models include language modelling ( Bengio et al . , 2003 ) , paraphrase detection ( Erk and Padó , 2008 ) , and dialogue analysis ( Kalchbrenner and Blunsom , 2013 ) .

Within a monolingual context , the distributional hypothesis ( Firth , 1957 ) forms the basis of most approaches for learning word representations .

In this work , we extend this hypothesis to multilingual data and joint-space embeddings .

We present a novel unsupervised technique for learning semantic representations that leverages parallel corpora and employs semantic transfer through compositional representations .

Unlike most methods for learning word representations , which are restricted to a single language , our approach learns to represent meaning across languages in a shared multilingual semantic space .

First , we show that for cross-lingual document classification on the Reuters RCV1 / RCV2 corpora ( Lewis et al . , 2004 ) , we outperform the prior state of the art ( Klementiev et al . , 2012 ) .

Second , we also present classification results on a massively multilingual corpus which we derive from the TED corpus ( Cettolo et al . , 2012 ) .

The results on this task , in comparison with a number of strong baselines , further demonstrate the relevance of our approach and the success of our method in learning multilingual semantic representations over a wide range of languages .

To summarize , we have presented a novel method for learning multilingual word embeddings using parallel data in conjunction with a multilingual objective function for compositional vector models .

This approach extends the distributional hypothesis to multilingual joint-space representations .

Coupled with very simple composition functions , vectors learned with this method outperform the state of the art on the task of cross-lingual document classification .

Further experiments and analysis support our hypothesis that bilingual signals are a useful tool for learning distributed representations by enabling models to abstract away from mono-lingual surface realisations into a deeper semantic space .


When translating among languages that differ substantially in word order , machine translation ( MT ) systems benefit from syntactic preordering-an approach that uses features from a syntactic parse to permute source words into a target-language-like order .

This paper presents a method for inducing parse trees automatically from a parallel corpus , instead of using a supervised parser trained on a treebank .

These induced parses are used to preorder source sentences .

We demonstrate that our induced parser is effective : it not only improves a state-of-the-art phrase-based system with integrated reordering , but also approaches the performance of a recent preordering method based on a supervised parser .

These results show that the syntactic structure which is relevant to MT pre-ordering can be learned automatically from parallel text , thus establishing a new application for unsupervised grammar induction .

Recent work in statistical machine translation ( MT ) has demonstrated the effectiveness of syntactic preordering : an approach that permutes source sentences into a target-like order as a pre-processing step , using features of a source-side syntactic parse ( Collins et al . , 2005 ; Xu et al . , 2009 ) .

Syntactic pre-ordering is particularly effective at applying structural transformations , such as the ordering change from a subject-verb-object ( SVO ) language like English to a subject-object-verb ( SOV ) language like Japanese .

However , state-of-the-art pre-ordering methods require a supervised syntactic parser to provide structural information about each sentence .

We propose a method that learns both a parsing model and a reordering model directly from a word-aligned parallel corpus .

Our approach , which we call Structure Induction for Reordering ( STIR ) , requires no syntactic annotations to train , but approaches the performance of a recent syntactic pre-ordering method in a large-scale English-Japanese MT system .

STIR predicts a pre-ordering via two pipelined models : ( 1 ) parsing and ( 2 ) tree reordering .

The first model induces a binary parse , which defines the space of possible reorderings .

In particular , only trees that properly separate verbs from their object noun phrases will license an SVO to SOV transformation .

The second model locally permutes this tree .

Our approach resembles work with binary synchronous grammars ( Wu , 1997 ) , but is distinct in its emphasis on monolingual parsing as a first phase , and in selecting reorderings without the aid of a target-side language model .

The parsing model is trained to maximize the conditional likelihood of trees that license the reorderings implied by observed word alignments in a parallel corpus .

This objective differs from those of previous grammar induction models , which typically focus on succinctly explaining the observed source language corpus via latent hierarchical structure ( Pereira and Schabes , 1992 ; Klein and Manning , 2002 ) .

Our convex objective allows us to train a feature-rich log-linear parsing model , even without supervised treebank data .

Focusing on pre-ordering for MT leads to a new perspective on the canonical NLP task of grammar induction-one which marries the wide-spread scientific interest in unsupervised parsing models with a clear application and extrinsic evaluation methodology .

To support this perspective , we highlight several avenues of future research throughout the paper .

We evaluate STIR in a large-scale English-Japanese machine translation system .

We measure how closely our predicted reorderings match those implied by hand-annotated word alignments .

STIR approaches the performance of the state-of-the-art pre-ordering method described in Genzel ( 2010 ) , which learns reordering rules for supervised treebank parses .

STIR gives a translation improvement of 3.84 BLEU over a standard phrase-based system with an integrated reordering model .

We have demonstrated that induced parses suffice for pre-ordering .

We hope that future work in grammar induction will also consider pre-ordering as an extrinsic evaluation .


Recently , neural network models for natural language processing tasks have been increasingly focused on for their ability of alleviating the burden of manual feature engineering .

However , the previous neural models can not extract the complicated feature compositions as the traditional methods with discrete features .

In this paper , we propose a gated recursive neural network ( GRNN ) for Chinese word segmentation , which contains reset and update gates to incorporate the complicated combinations of the context characters .

Since GRNN is relative deep , we also use a supervised layer-wise training method to avoid the problem of gradient diffusion .

Experiments on the benchmark datasets show that our model outperforms the previous neural network models as well as the state-of-the-art methods .

Unlike English and other western languages , Chinese do not delimit words by white-space .

Therefore , word segmentation is a preliminary and important pre-process for Chinese language processing .

Most previous systems address this problem by treating this task as a sequence labeling problem and have achieved great success .

Due to the nature of supervised learning , the performance of these models is greatly affected by the design of features .

These features are explicitly represented by the different combinations of context characters , which are based on linguistic intuition and statistical information .

However , the number of features could be so large that the result models are too large to use in practice and prone to overfit on training corpus .

Figure 1 : Illustration of our model for Chinese word segmentation .

The solid nodes indicate the active neurons , while the hollow ones indicate the suppressed neurons .

Specifically , the links denote the information flow , where the solid edges denote the acceptation of the combinations while the dashed edges means rejection of that .

As shown in the right figure , we receive a score vector for tagging target character " 地 " by incorporating all the combination information .

Recently , neural network models have been increasingly focused on for their ability to minimize the effort in feature engineering .

Collobert et al . ( 2011 ) developed a general neural network architecture for sequence labeling tasks .

Following this work , many methods ( Zheng et al . , 2013 ; Pei et al . , 2014 ; Qi et al . , 2014 ) applied the neural network to Chinese word segmentation and achieved a performance that approaches the state-of-the-art methods .

However , these neural models just concatenate the embeddings of the context characters , and feed them into neural network .

Since the concatenation operation is relatively simple , it is difficult to model the complicated features as the traditional discrete feature based models .

Although the complicated interactions of inputs can be modeled by the deep neural network , the previous neural model shows that the deep model can not outperform the one with a single non-linear model .

Therefore , the neural model only captures the interactions by the simple transition matrix and the single non-linear transformation .

These dense features extracted via these simple interactions are not nearly as good as the substantial discrete features in the traditional methods .

In this paper , we propose a gated recursive neural network ( GRNN ) to model the complicated combinations of characters , and apply it to Chinese word segmentation task .

Inspired by the success of gated recurrent neural network ( Chung et al . , 2014 ) , we introduce two kinds of gates to control the combinations in recursive structure .

We also use the layer-wise training method to avoid the problem of gradient diffusion , and the dropout strategy to avoid the overfitting problem .

Figure 1 gives an illustration of how our approach models the complicated combinations of the context characters .

Given a sentence " 雨 ( Rainy ) 天 ( Day ) 地面 ( Ground ) 积水 ( Accumulated water ) " , the target character is " 地 " .

This sentence is very complicated because each consecutive two characters can be combined as a word .

To predict the label of the target character " 地 " under the given context , GRNN detects the combinations recursively from the bottom layer to the top .

Then , we receive a score vector of tags by incorporating all the combination information in network .

The contributions of this paper can be summarized as follows :

• We propose a novel GRNN architecture to model the complicated combinations of the context characters .

GRNN can select and preserve the useful combinations via reset and update gates .

These combinations play a similar role in the feature engineering of the traditional methods with discrete features .

• We evaluate the performance of Chinese word segmentation on PKU , MSRA and CTB6 benchmark datasets which are commonly used for evaluation of Chinese word segmentation .

Experiment results show that our model outperforms other neural network models , and achieves state-of-the-art performance .

In this paper , we propose a gated recursive neural network ( GRNN ) to explicitly model the combinations of the characters for Chinese word segmentation task .

Each neuron in GRNN can be regarded as a different combination of the input characters .

Thus , the whole GRNN has an ability to simulate the design of the sophisticated features in traditional methods .

Experiments show that our proposed model outperforms the state-of-the-art methods on three popular benchmark datasets .

Despite Chinese word segmentation being a specific case , our model can be easily generalized and applied to other sequence labeling tasks .

In future work , we would like to investigate our proposed GRNN on other sequence labeling tasks .


In contrast to recent advances focusing on highlevel representation learning across modalities , in this work we present a self-supervised learning framework that is able to learn a representation that captures finer levels of granularity across different modalities such as concepts or events represented by visual objects or spoken words .

Our framework relies on a discretized embedding space created via vector quantization that is shared across different modalities .

Beyond the shared embedding space , we propose a Cross-Modal Code Matching objective that forces the representations from different views ( modalities ) to have a similar distribution over the discrete embedding space such that cross-modal objects / actions localization can be performed without direct supervision .

We show that the proposed discretized multi-modal finegrained representation ( e.g. , pixel / word / frame ) can complement high-level summary representations ( e.g. , video / sentence / waveform ) for improved performance on cross-modal retrieval tasks .

We also observe that the discretized representation uses individual clusters to represent the same semantic concept across modalities .

Toddlers acquire much of their knowledge through grounded learning -visual concepts can be acquired through language , and language acquisition emerges through visual interaction .

Inspired by this type of grounded learning , a rich body of representation learning research ( Harwath et al . , 2018 ; Miech et al . , 2020 ;

Alayrac et al . , 2020 ; Monfort et al . , 2021 ; Luo et al . , 2021 ) has been exploring the potential to learn from multi-modal data such as video-text , video-audio , and image-audio pairs .

These works typically focus on learning a joint embedding space between different modalities , in which high-level summary representations are extracted as embedding vectors .

These embedding vectors often represent entire video clips , spoken utterances , or sentences as single vectors , and can be useful on tasks such as cross-modal data retrieval , e.g. , finding the most similar visual scene according to a spoken language description .

The predominant approach to learning these embedding vectors is to use modality-independent encoders , and while this has been successful for downstream retrieval tasks , it makes it difficult to compare the activations of the encoders from different modalities .

Further , the space of continuous embedding vectors is unbounded , which makes interpreting the learned representations challenging .

To this end , we propose to jointly learn highlevel embedding vector representations with a finegrained discrete embedding space that is shared across different modalities .

The discrete embedding space enables model interpretability since there are a finite number of embedding vectors which are shared across modalities .

Besides the shared embedding space , we propose a Cross-Modal Code Matching ( CMCM ) objective that guides the embedding space to capture cross-modal correspondences of concepts , actions , and words .

This not only improves downstream performance on retrieval , but also allows us to better interpret what the model recognized through cross-modal grounded learning .

To verify the effectiveness of our proposed learning framework , we conducted experiments in several cross-modal domains , including video-text , video-audio , and image-audio .

We found consistent improvements over baseline models , verifying that the gain was not restricted to the particular choice of network architecture , input modalities , or dataset .

We also demonstrate the interpretability of the fine-grained discrete representations by showing the cross-modal relations between the embedding vectors and semantic concepts appearing in the input modalities .

Our approach also enables cross-modal concept localization without requiring any labels during training .

Figure 1 : An overview of the proposed framework .

The proposed shared discrete embedding space ( green region , described in Section 2.2 ) is based on a cross-modal representation learning paradigm ( blue / yellow regions , described in Section 2.1 ) .

The proposed Cross-Modal Code Matching L CMCM objective is detailed in Section 2.3 and Figure 2 .

In this paper , we proposed a framework for crossmodal representation learning with a discrete embedding space that is shared amongst different modalities and enables model interpretability .

We also propose a Cross-Modal Code Matching objective that encourages models to represent crossmodel semantic concepts in the embedding space .

Combining our discrete embedding space and objective with existing cross-modal representation learning models improves retrieval performance on video-text , video-audio , and image-audio datasets .

We also analyze the shared embedding space and find that semantically related video and audio inputs tend to use the same codewords .


PCFGs with latent annotations have been shown to be a very effective model for phrase structure parsing .

We present a Bayesian model and algorithms based on a Gibbs sampler for parsing with a grammar with latent annotations .

For PCFG-LA , we present an additional Gibbs sampler algorithm to learn annotations from training data , which are parse trees with coarse ( unannotated ) symbols .

We show that a Gibbs sampling technique is capable of parsing sentences in a wide variety of languages and producing results that are on-par with or surpass previous approaches .

Our results for Kinyarwanda and Malagasy in particular demonstrate that low-resource language parsing can benefit substantially from a Bayesian approach .

Despite great progress over the past two decades on parsing , relatively little work has considered the problem of creating accurate parsers for low-resource languages .

Existing work in this area focuses primarily on approaches that use some form of cross-lingual bootstrapping to improve performance .

For instance , Hwa et al . ( 2005 ) use a parallel Chinese / English corpus and an English dependency grammar to induce an annotated Chinese corpus in order to train a Chinese dependency grammar .

Kuhn ( 2004b ) also considers the benefits of using multiple languages to induce a monolingual grammar , making use of a measure for data reliability in order to weight training data based on confidence of annotation .

Bootstrapping approaches such as these achieve markedly improved results , but they are dependent on the existence of a parallel bilingual corpus .

Very few such corpora are readily available , particularly for low-resource languages , and creating such corpora obviously presents a challenge for many practical applications .

Kuhn ( 2004a ) shows some of the difficulty in handling low-resource languages by examining various tasks using Q'anjob'al as an example .

Another approach is that of Bender et al . ( 2002 ) , who take a more linguistically-motivated approach by making use of linguistic universals to seed newly developed grammars .

This substantially reduces the effort by making it unnecessary to learn the basic parameters of a language , but it lacks the robustness of grammars learned from data .

Recent work on Probabilistic Context-Free Grammars with latent annotations ( PCFG-LA ) ( Matsuzaki et al . , 2005 ; Petrov et al . , 2006 ) have shown them to be effective models for syntactic parsing , especially when less training material is available ( Liang et al . , 2009 ; Shindo et al . , 2012 ) .

The coarse nonterminal symbols found in vanilla PCFGs are refined by latent variables ; these latent annotations can model subtypes of grammar symbols that result in better grammars and enable better estimates of grammar productions .

In this paper , we provide a Gibbs sampler for learning PCFG-LA models and show its effectiveness for parsing lowresource languages such as Malagasy and Kinyawanda .

Previous PCFG-LA work focuses on the problem of parameter estimation , including expectationmaximization ( EM ) ( Matsuzaki et al . , 2005 ; Petrov et al . , 2006 ) , spectral learning ( Cohen et al . , 2012 ; Cohen et al . , 2013 ) , and variational inference ( Liang et al . , 2009 ; Wang and Blunsom , 2013 ) .

Regardless of inference method , previous work has used the same method to parse new sentences : a Viterbi parse under a new sentence-specific PCFG obtained from an approximation of the original grammar ( Matsuzaki et al . , 2005 ) .

Here , we provide an alternative approach to parsing new sentences : an extension of the Gibbs sampling algorithm of Johnson et al . ( 2007 ) , which learns rule probabilities in an unsupervised PCFG .

We use a Gibbs sampler to collect sampled trees theoretically distributed from the true posterior distribution in order to parse .

Priors in a Bayesian model can control the sparsity of grammars ( which the insideoutside algorithm fails to do ) , while naturally incorporating smoothing into the model ( Johnson et al . , 2007 ; Liang et al . , 2009 ) .

We also build a Bayesian model for parsing with a treebank , and incorporate information from training data as a prior .

Moreover , we extend the Gibbs sampler to learn and parse PCFGs with latent annotations .

Learning the latent annotations is a compute-intensive process .

We show how a small amount of training data can be used to bootstrap : after running a large number of sampling iterations on a small set , the resulting parameters are used to seed a smaller number of iterations on the full training data .

This allows us to employ more latent annotations while maintaining reasonable training times and still making full use of the available training data .

To determine the cross-linguistic applicability of these methods , we evaluate on a wide variety of languages with varying amounts of available training data .

We use English and Chinese as examples of languages with high data availability , while Italian , Malagasy , and Kinyarwanda provide examples of languages with little available data .

We find that our technique comes near state of the art results on large datasets , such as those for Chinese and English , and it provides excellent results on limited datasets -both artificially limited in the case of English , and naturally limited in the case of Italian , Malagasy , and Kinyarwanda .

This , combined with its ability to run off-the-shelf on new languages without any supporting materials such as parallel corpora , make it a valuable technique for the parsing of low-resource languages .

Our experiments demonstrate that sampling vanilla PCFGs , as well as PCFGs with latent annotations , is feasible with the use of a Gibbs sampler technique and produces results that are in line with previous parsers on controlled test sets .

Our results also show that our methods are effective on a wide variety of languages-including two low-resource languageswith no language-specific model modifications needed .

Additionally , although not a uniform winner , the Gibbs-PCFG shows a propensity for performing well on naturally small corpora ( here , KIN / MLG ) .

The exact reason for this remains slightly unclear , but the fact that a similar advantage is not found for extremely small versions of large corpora indicates that our approach may be particularly well-suited for application in real low-resource environments as opposed to a sim-

Having established this procedure and its relative tolerance for low amounts of data , we would like to extend the model to make use of partial bracketing information instead of complete trees , perhaps in the form of Fragmentary Unlabeled Dependency Grammar annotations ( Schneider et al . , 2013 ) .

This would allow the sampling procedure to potentially operate using corpora with lighter annotations than full trees , making initial annotation effort not quite as heavy and potentially increasing the amount of available data for low-resource languages .

Additionally , using the expert partial annotations to help restrict the sample space could provide good gains in terms of training time .


In the face of sparsity , statistical models are often interpolated with lower order ( backoff ) models , particularly in Language Modeling .

In this paper , we argue that there is a relation between the higher order and the backoff model that must be satisfied in order for the interpolation to be effective .

We show that in n-gram models , the relation is trivially held , but in models that allow arbitrary clustering of context ( such as decision tree models ) , this relation is generally not satisfied .

Based on this insight , we also propose a generalization of linear interpolation which significantly improves the performance of a decision tree language model .

A prominent use case for Language Models ( LMs ) in NLP applications such as Automatic Speech Recognition ( ASR ) and Machine Translation ( MT ) is selection of the most fluent word sequence among multiple hypotheses .

Statistical LMs formulate the problem as the computation of the model 's probability to generate the word sequence w 1 w 2 . . .

w m ≡ w m 1 , assuming that higher probability corresponds to more fluent hypotheses .

LMs are often represented in the following generative form :

In the following discussion , we will refer to the function p(w

Note the context space for this function , w i-1 1 is arbitrarily long , necessitating some independence assumption , which usually consists of reducing the relevant context to n -1 immediately preceding tokens :

These distributions are typically estimated from observed counts of n-grams w i i-n+1 in the training data .

The context space is still far too large ; therefore , the models are recursively smoothed using lower order distributions .

For instance , in a widely used n-gram LM , the probabilities are estimated as follows :

In addition to n-gram models , there are many other ways to estimate probability distributions p(w

i |w i-1 i-n+1 ) ; in this work , we are particularly interested in models involving decision trees ( DTs ) .

As in n-gram models , DT models also often utilize interpolation with lower order models ; however , there are issues concerning the interpolation which arise from the fact that decision trees permit arbitrary clustering of context , and these issues are the main subject of this paper .

The main contribution of this paper is the insight that in the standard recursive backoff there is an implied relation between the backoff and the higher order models , which is essential for adequate performance .

When this relation is not satisfied other interpolation methods should be employed ; hence , we propose a generalization of linear interpolation that significantly outperforms the standard form in such a scenario .


In this paper , we present a recursive neural network ( RNN ) model that works on a syntactic tree .

Our model differs from previous RNN models in that the model allows for an explicit weighting of important phrases for the target task .

We also propose to average parameters in training .

Our experimental results on semantic relation classification show that both phrase categories and task-specific weighting significantly improve the prediction accuracy of the model .

We also show that averaging the model parameters is effective in stabilizing the learning and improves generalization capacity .

The proposed model marks scores competitive with state-of-the-art RNN-based models .

Recursive Neural Network ( RNN ) models are promising deep learning models which have been applied to a variety of natural language processing ( NLP ) tasks , such as sentiment classification , compound similarity , relation classification and syntactic parsing ( Hermann and Blunsom , 2013 ; Socher et al . , 2012 ; Socher et al . , 2013 ) .

RNN models can represent phrases of arbitrary length in a vector space of a fixed dimension .

Most of them use minimal syntactic information ( Socher et al . , 2012 ) .

Recently , Hermann and Blunsom ( 2013 ) proposed a method for leveraging syntactic information , namely CCG combinatory operators , to guide composition of phrases in RNN models .

While their models were successfully applied to binary sentiment classification and compound similarity tasks , there are questions yet to be answered , e.g. , whether such enhancement is beneficial in other NLP tasks as well , and whether a similar improvement can be achieved by using syntactic information of more commonly available types such as phrase categories and syntactic heads .

In this paper , we present a supervised RNN model for a semantic relation classification task .

Our model is different from existing RNN models in that important phrases can be explicitly weighted for the task .

Syntactic information used in our model includes part-of-speech ( POS ) tags , phrase categories and syntactic heads .

POS tags are used to assign vector representations to word-POS pairs .

Phrase categories are used to determine which weight matrices are chosen to combine phrases .

Syntactic heads are used to determine which phrase is weighted during combining phrases .

To incorporate task-specific information , phrases on the path between entity pairs are further weighted .

The second contribution of our work is the introduction of parameter averaging into RNN models .

In our preliminary experiments , we observed that the prediction performance of the model often fluctuates significantly between training iterations .

This fluctuation not only leads to unstable performance of the resulting models , but also makes it difficult to fine-tune the hyperparameters of the model .

Inspired by Swersky et al . ( 2010 ) , we propose to average the model parameters in the course of training .

A recent technique for deep learning models of similar vein is dropout ( Hinton et al . , 2012 ) , but averaging is simpler to implement .

Our experimental results show that our model per-Figure 1 : A recursive representations of a phrase " a word vector " with POS tags of the words ( DT , NN and NN respectively ) .

For example , the two word-POS pairs " word NN " and " vector NN " with a syntactic category N are combined to represent the phrase " word vector " .

By averaging the model parameters , our model achieves performance competitive with the MV-RNN model in Socher et al . ( 2012 ) , without using computationally expensive word-dependent matrices .

We have presented an averaged RNN model for semantic relation classification .

Our experimental results show that syntactic information such as phrase categories and heads improves the performance , and the task-specific weighting is also beneficial .

The results also demonstrate that averaging the model parameters not only stabilizes the learning but also improves the generalization capacity of the model .

As future work , we plan to combine deep learning models with richer information such as predicateargument structures .


Coreferential information of a candidate , such as the properties of its antecedents , is important for pronoun resolution because it reflects the salience of the candidate in the local discourse .

Such information , however , is usually ignored in previous learning-based systems .

In this paper we present a trainable model which incorporates coreferential information of candidates into pronoun resolution .

Preliminary experiments show that our model will boost the resolution performance given the right antecedents of the candidates .

We further discuss how to apply our model in real resolution where the antecedents of the candidate are found by a separate noun phrase resolution module .

The experimental results show that our model still achieves better performance than the baseline .

In recent years , supervised machine learning approaches have been widely explored in reference resolution and achieved considerable success ( Ge et al . , 1998 ; Soon et al . , 2001 ; Ng and Cardie , 2002 ; Strube and Muller , 2003 ; Yang et al . , 2003 ) .

Most learning-based pronoun resolution systems determine the reference relationship between an anaphor and its antecedent candidate only from the properties of the pair .

The knowledge about the context of anaphor and antecedent is nevertheless ignored .

However , research in centering theory ( Sidner , 1981 ; Grosz et al . , 1983 ; Grosz et al . , 1995 ; Tetreault , 2001 ) has revealed that the local focusing ( or centering ) also has a great effect on the processing of pronominal expressions .

The choices of the antecedents of pronouns usually depend on the center of attention throughout the local discourse segment ( Mitkov , 1999 ) .

To determine the salience of a candidate in the local context , we may need to check the coreferential information of the candidate , such as the existence and properties of its antecedents .

In fact , such information has been used for pronoun resolution in many heuristicbased systems .

The S-List model ( Strube , 1998 ) , for example , assumes that a co-referring candidate is a hearer-old discourse entity and is preferred to other hearer-new candidates .

In the algorithms based on the centering theory ( Brennan et al . , 1987 ; Grosz et al . , 1995 ) , if a candidate and its antecedent are the backwardlooking centers of two subsequent utterances respectively , the candidate would be the most preferred since the CONTINUE transition is always ranked higher than SHIFT or RETAIN .

In this paper , we present a supervised learning-based pronoun resolution system which incorporates coreferential information of candidates in a trainable model .

For each candidate , we take into consideration the properties of its antecedents in terms of features ( henceforth backward features ) , and use the supervised learning method to explore their influences on pronoun resolution .

In the study , we start our exploration on the capability of the model by applying it in an ideal environment where the antecedents of the candidates are correctly identified and the backward features are optimally set .

The experiments on MUC-6 ( 1995 ) and MUC-7 ( 1998 ) corpora show that incorporating coreferential information of candidates boosts the system performance significantly .

Further , we apply our model in the real resolution where the antecedents of the candidates are provided by separate noun phrase resolution modules .

The experimental results show that our model still outperforms the baseline , even with the low recall of the non-pronoun resolution module .

The remaining of this paper is organized as follows .

Section 2 discusses the importance of the coreferential information for candidate evaluation .

Section 3 introduces the baseline learning framework .

Section 4 presents and evaluates the learning model which uses backward fea-tures to capture coreferential information , while Section 5 proposes how to apply the model in real resolution .

Section 6 describes related research work .

Finally , conclusion is given in Section 7 .

In this paper we have proposed a model which incorporates coreferential information of candi-dates to improve pronoun resolution .

When evaluating a candidate , the model considers its adjacent antecedent by describing its properties in terms of backward features .

We first examined the effectiveness of the model by applying it in an optimal environment where the closest antecedent of a candidate is obtained correctly .

The experiments show that it boosts the success rate of the baseline system for both MUC-6 ( 4.7 % ) and MUC-7 ( 3.5 % ) .

Then we proposed how to apply our model in the real resolution where the antecedent of a non-pronoun is found by an additional non-pronoun resolution module .

Our model can still produce Success improvement ( 4.7 % for MUC-6 and 1.8 % for MUC-7 ) against the baseline system , despite the low recall of the non-pronoun resolution module .

In the current work we restrict our study only to pronoun resolution .

In fact , the coreferential information of candidates is expected to be also helpful for non-pronoun resolution .

We would like to investigate the influence of the coreferential factors on general NP reference resolution in our future work .


Modelling prosody variation is critical for synthesizing natural and expressive speech in endto-end text-to-speech ( TTS ) systems .

In this paper , a cross-utterance conditional VAE ( CUC-VAE ) is proposed to estimate a posterior probability distribution of the latent prosody features for each phoneme by conditioning on acoustic features , speaker information , and text features obtained from both past and future sentences .

At inference time , instead of the standard Gaussian distribution used by VAE , CUC-VAE allows sampling from an utterance-specific prior distribution conditioned on cross-utterance information , which allows the prosody features generated by the TTS system to be related to the context and is more similar to how humans naturally produce prosody .

The performance of CUC-VAE is evaluated via a qualitative listening test for naturalness , intelligibility and quantitative measurements , including word error rates and the standard deviation of prosody attributes .

Experimental results on LJ-Speech and LibriTTS data show that the proposed CUC-VAE TTS system improves naturalness and prosody diversity with clear margins .

Recently , abundant research have been performed on modelling variations other than the input text in synthesized speech such as background noise , speaker information , and prosody , as those directly influence the naturalness and expressiveness of the generated audio .

Prosody , as the focus of this paper , collectively refers to the stress , intonation , and rhythm in speech , and has been an increasingly popular research aspect in end-to-end TTS systems ( van den Oord et al . , 2016 ; Wang et al . , 2017 ; Stanton et al . , 2018 ; Elias et al . , 2021 ; Chen et al . , 2021 ) .

Some previous work captured prosody features ex-plicitly using either style tokens or variational autoencoders ( VAEs ) ( Kingma and Welling , 2014 ; Hsu et al . , 2019a ) which encapsulate prosody information into latent representations .

Recent work achieved fine-grained prosody modelling and control by extracting prosody features at phoneme or word-level ( Lee and Kim , 2019 ; Sun et al . , 2020a , b ) .

However , the VAE-based TTS system lacks control over the latent space where the sampling is performed from a standard Gaussian prior during inference .

Therefore , recent research ( Dahmani et al . , 2019 ; Karanasou et al . , 2021 ) employed a conditional VAE ( CVAE ) ( Sohn et al . , 2015 ) to synthesize speech from a conditional prior .

Meanwhile , pre-trained language model ( LM ) such as bidirectional encoder representation for Transformers ( BERT ) ( Devlin et al . , 2019 ) has also been applied to TTS systems ( Hayashi et al . , 2019 ; Kenter et al . , 2020 ; Jia et al . , 2021 ; Futamata et al . , 2021 ; Cong et al . , 2021 ) to estimate prosody attributes implicitly from pre-trained text representations within the utterance or the segment .

Efforts have been devoted to include cross-utterance information in the input features to improve the prosody modelling of auto-regressive TTS ( Xu et al . , 2021 ) .

To generate more expressive prosody , while maintaining high fidelity in synthesized speech , a cross-utterance conditional VAE ( CUC-VAE ) component is proposed , which is integrated into and jointly optimised with FastSpeech 2 ( Ren et al . , 2021 ) , a commonly used non-autoregressive end-toend TTS system .

Specifically , the CUC-VAE TTS system consists of cross-utterance embedding ( CUembedding ) and cross-utterance enhanced CVAE ( CU-enhanced CVAE ) .

The CU-embedding takes BERT sentence embeddings from surrounding utterances as inputs and generates phoneme-level CUembedding using a multi-head attention ( Vaswani et al . , 2017 ) layer where attention weights are derived from the encoder output of each phoneme as well as the speaker information .

The CU-enhanced CVAE is proposed to improve prosody variation and to address the inconsistency between the standard Gaussian prior , which the VAE-based TTS system is sampled from , and the true prior of speech .

Specifically , the CU-enhanced CVAE is a fine-grained VAE that estimates the posterior of latent prosody features for each phoneme based on acoustic features , cross-utterance embedding , and speaker information .

It improves the encoder of standard VAE with an utterance-specific prior .

To match the inference with training , the utterancespecific prior , jointly optimised with the system , is conditioned on the output of CU-embedding .

Latent prosody features are sampled from the derived utterance-specific prior instead of a standard Gaussian prior during inference .

The proposed CUC-VAE TTS system was evaluated on the LJ-Speech read English data and the LibriTTS English audiobook data .

In addition to the sample naturalness measured via subjective listening tests , the intelligibility is measured using word error rate ( WER ) from an automatic speech recognition ( ASR ) system , and diversity in prosody was measured by calculating standard deviations of prosody attributes among all generated audio samples of an utterance .

Experimental results showed that the system with CUC-VAE achieved a much better prosody diversity while improving both the naturalness and intelligibility compared to the standard FastSpeech 2 baseline and two variants .

The rest of this paper is organised as follows .

Section 2 introduces the background and related work .

Section 3 illustrates the proposed CUC-VAE TTS system .

Experimental setup and results are shown in Section 4 and Section 5 , with conclusions in Section 6 .

In this paper , a non-autoregressive CUC-VAE TTS system was proposed to synthesize speech with better naturalness and more prosody diversity .

CUC-VAE TTS system estimated the posterior distribution of latent prosody features for each phone based on cross-utterance information in addition to the acoustic features and speaker information .

The generated audio was sampled from an utterancespecific prior distribution , approximated based on cross-utterance information .

Experiments were conducted to evaluate the proposed CUC-VAE TTS system with metrics including MOS , preference rate , WER , and the standard deviation of prosody attributes .

Experiment results showed that the proposed CUC-VAE TTS system improved both the naturalness and prosody diversity in the generated audio samples , which outperformed the baseline in all metrics with clear margins .


Responsing with image has been recognized as an important capability for an intelligent conversational agent .

Yet existing works only focus on exploring the multimodal dialogue models which depend on retrieval-based methods , but neglecting generation methods .

To fill in the gaps , we first present a new task : multimodal dialogue response generation ( MDRG)given the dialogue context , one model needs to generate a text or an image as response .

Learning such a MDRG model often requires multimodal dialogues containing both texts and images which are difficult to obtain .

Motivated by the challenge in practice , we consider MDRG under a natural assumption that only limited training examples are available .

Under such a low-resource setting , we devise a novel conversational agent , Divter , in order to isolate parameters that depend on multimodal dialogues from the entire generation model .

By this means , the major part of the model can be learned from a large number of text-only dialogues and textimage pairs respectively , then the whole parameters can be well fitted using just a few training examples .

Extensive experiments demonstrate our method achieves state-of-the-art results in both automatic and human evaluation , and can generate informative text and high-resolution image responses .

With the development of instant messaging technology in the recent decades , the intermediary of online conversation has also changed from pure text to a variety of visual modalities ( e.g. , image , gif animation , short video ) .

Similar to communicating by the messenger tools ( e.g. , Facebook , WhatsApp , WeChat ) in reality , an excellent intelligent conversational agent should not only be able to converse freely with plain text , but also have the ability to perceive and share the real visual physical world .

Although recently some large-scale pre-trained text-only dialogue generation models , such as Di-aloGPT ( Zhang et al . , 2020 ) , Blender ( Roller et al . , 2021 ) , Meena ( Adiwardana et al . , 2020 ) , have shown excellent performance , they still can not rely exclusively on plain text to completely simulate the rich experience of visual perception .

Recently , various vision-language tasks have been introduced and attracted widespread attention , such as visual question answering ( Ren et al . , 2015 ; Lu et al . , 2016 ; Anderson et al . , 2018 ; Li et al . , 2019a ; Huang et al . , 2020 ) , image captioning ( Xu et al . , 2015 ;

Anderson et al . , 2016 ; Ghanimifard and Dobnik , 2019 ; Cornia et al . , 2020 ) , image-grounded dialogue ( Das et al . , 2017 ; Yang et al . , 2021 ; Agarwal et al . , 2020 ; Qi et al . , 2020 ; Chen et al . , 2021 ; Liang et al . , 2021 ) .

Specifically , in human conversations , the images can easily show rich visual perception , which is hard to be expressed by plain text .

As the example shown in Figure 1 , images are required in at least three circumstances : ( i ) the other speaker has little knowledge ( e.g. , colorful Burano , in the 1st image ) of the objects only you had seen ; ( ii ) to share more details ( e.g. , red wine and pasta , in the 2nd image ) of the objects even you have common knowledge of them ; ( iii ) to express your emotions ( e.g. , happy , in the 3rd image ) about a specific event .

An existing related task is photo sharing ( Zang et al . , 2021 ) , which aims to select and share the image based on the textual context , is a challenging task that requires models to understand the background story which complemented by human imaginations , rather than to locate related visual objects or explicitly mention main visible content in the image as the previous works do .

Zang et al . ( 2021 ) propose a retrieval-based method to resolve the above challenge .

However , the performance of the retrieval-based method is limited in specific domains by the size of the pre-constructed conversational history repository , especially for long-tail contexts that are not covered in the history , where the set of image responses of a retrieval system is also fixed .

On the other hand , a better way is to generate a new one accordingly .

In this paper , we formulate a new problem : Multimodal Dialogue Response Generation ( MDRG ) , that is , given the dialogue context , the model should not only generate a pure text response but also have the capacity to generate a multimodal response ( e.g. , containing both image and text ) .

We argue that there are still some hindrances to application , since ( 1 ) the sophisticated neural end-to-end architecture will overfit to very few well-annotated training data ( e.g. , a few existing 10k multimodal dialogues ) .

Evidence is that when discussing the topics outside the training data domain , its performance drops dramatically ; and ( 2 ) as human effort is expensive , it is not easy to collect enough training data for a new domain .

Based on the above facts , we take a step further to extend the assumption of MDRG to a low-resource setting where only a few multimodal dialogues are available .

To tackle the above challenges , our key idea is to make parameters that rely on multimodal dialogues small and independent by disentangling textual response generation and image response generation , and thus we can learn the major part of the generation model from text-only dialogues and < image description , image > pairs that are much easier to be obtained .

Specifically , we present Divter , a novel conversational agent powered by large-scale visual world experiences .

As shown in Figure 2 , our Divter is made up of two Transformer-based ( Vaswani et al . , 2017a )

components : a multimodal dialogue response generator , and a text-to-image translator .

Divter takes the dialogue context as input , then generates a textual sequence which may contains a text response or a textual image description or both of them .

The text-to-image translator takes above image description as condition , then generates a realistic and consistent high resolution image .

Both components are independent with the opposite knowledge , and thus can be pre-trained using a large number of text-only dialogues and the < image description , image > pairs respectively .

The end-to-end Divter depends on the multimodal dialogues constructed as the tuple : ( dialogue context , text response / < image description , image > ) , but the joint learning and estimation of the two components just require a few training examples depending on specific domains .

Contributions of this work are three-fold :

• To the best of our knowledge , it is the first work on the multimodal dialogue response generation .

We explore the task under a lowresource setting where only a few multimodal dialogues are assumed available .

• We present Divter , a novel conversational agent which can effectively understand dialogue context and generate informative text and high-resolution image responses .

• Extensive experiments on PhotoChat Corpus ( Zang et al . , 2021 ) indicate the effectiveness of Divter , it achieves a significant improvement with pure text dialogue generation model and retrieval-based image sharing method .

In this paper , we explore multimodal dialogue response generation under a low-resource setting .

To overcome the challenges from the new task and insufficient training data , we propose Divter , a neural conversational agent which incorporates text-toimage generation into text-only dialogue response generation , in which most parameters do not rely on the training data any more and can be estimated from large scale textual open domain dialogues and < image description , image > pairs .

Extensive experiments demonstrate Divter achieves state-of-the-art results in automatic and human evaluation .

In the future , we will explore more efficient methods to inject more modalities into response generation .


Aspect-based sentiment analysis involves the recognition of so called opinion target expressions ( OTEs ) .

To automatically extract OTEs , supervised learning algorithms are usually employed which are trained on manually annotated corpora .

The creation of these corpora is labor-intensive and sufficiently large datasets are therefore usually only available for a very narrow selection of languages and domains .

In this work , we address the lack of available annotated data for specific languages by proposing a zero-shot cross-lingual approach for the extraction of opinion target expressions .

We leverage multilingual word embeddings that share a common vector space across various languages and incorporate these into a convolutional neural network architecture for OTE extraction .

Our experiments with 5 languages give promising results : We can successfully train a model on annotated data of a source language and perform accurate prediction on a target language without ever using any annotated samples in that target language .

Depending on the source and target language pairs , we reach performances in a zero-shot regime of up to 77 % of a model trained on target language data .

Furthermore , we can increase this performance up to 87 % of a baseline model trained on target language data by performing cross-lingual learning from multiple source languages .

In recent years , there has been an increasing interest in developing sentiment analysis models that predict sentiment at a more fine-grained level than at the level of a complete document .

A paradigm coined as Aspect-based Sentiment Analysis ( ABSA ) addresses this need by defining the sentiment expressed in a text relative to an opinion target ( also called aspect ) .

Consider the following example from a restaurant review :

" Moules were excellent , lobster ravioli was VERY salty ! "

In this example , there are two sentiment statements , one positive and one negative .

The positive one is indicated by the word " excellent " and is expressed towards the opinion target " Moules " .

The second , negative sentiment , is indicated by the word " salty " and is expressed towards the " lobster ravioli " .

A key task within this fine-grained sentiment analysis consists of identifying so called opinion target expressions ( OTE ) .

In this paper , we are concerned with how to transfer classifiers trained on one domain to another domain .

In particular , we focus on the transfer of models across languages to alleviate the need for multilingual training data .

We propose a model that is capable of accurate zero-shot cross-lingual OTE extraction , thus reducing the reliance on annotated data for every language .

Similar to Upadhyay et al . ( 2018 ) , our model leverages multilingual word embeddings ( Smith et al . , 2017 ; Lample et al . , 2018 ) that share a common vector space across various languages .

The shared space allows us to transfer a model trained on source language data to predict OTEs in a target language for which no ( i.e. zero-shot setting ) or only small amounts of data are available , thus allowing to apply our model to under-resourced languages .

Our main contributions can be summarized as follows :

• We present the first approach for zero-shot cross-lingual opinion target extraction and achieve up to 87 % of the performance of a monolingual baseline .

• We investigate the benefit of using multi-ple source languages for cross-lingual learning and show that we can improve by 6 to 8 points in F 1 -Score compared to a model trained on a single source language .

• We investigate the benefit of augmenting the zero-shot approach with additional data points from the target language .

We observe that we can save hundreds of annotated data points by employing a cross-lingual approach .

• We compare two methods for obtaining cross-lingual word embeddings on the task .

In this work , we presented a method for crosslingual and zero-shot extraction of opinion target expressions which we evaluated on 5 languages .

Our approach uses multilingual word embeddings that are aligned into a single vector space to allow for cross-lingual transfer of models .

Using English as a source language in a zeroshot setting , our approach was able to reach an F 1score of 0.50 for Spanish and 0.46 for Dutch .

This corresponds to relative performances of 74 % and 77 % compared to a baseline system trained on target language data .

By using multiple source languages , we increased the zero-shot performance to F 1 -scores of 0.58 and 0.53 , respectively , which correspond to 85 % and 87 % in relative terms .

We investigated the benefit of augmenting the zeroshot approach with additional data points from the target language .

Here , we observed that we can save several hundreds of annotated data points by employing a cross-lingual approach .

Among the 5 considered languages , Turkish seemed to benefit the least from cross-lingual learning in all experiments .

The reason for this might be that Turkish is the only agglutinative language in the dataset .

Further , we compared two approaches for aligning multilingual word embeddings in a single vector space and found their results to vary for individual language pairs but to be comparable overall .

Lastly , we compared our multilingual model with the state-of-the-art for all languages and saw that we achieve competitive performances for some languages and even present the best system for Russian and Turkish .


Neural models have achieved remarkable success on relation extraction ( RE ) benchmarks .

However , there is no clear understanding which type of information affects existing RE models to make decisions and how to further improve the performance of these models .

To this end , we empirically study the effect of two main information sources in text : textual context and entity mentions ( names ) .

We find that ( i ) while context is the main source to support the predictions , RE models also heavily rely on the information from entity mentions , most of which is type information , and ( ii ) existing datasets may leak shallow heuristics via entity mentions and thus contribute to the high performance on RE benchmarks .

Based on the analyses , we propose an entity-masked contrastive pre-training framework for RE to gain a deeper understanding on both textual context and type information while avoiding rote memorization of entities or use of superficial cues in mentions .

We carry out extensive experiments to support our views , and show that our framework can improve the effectiveness and robustness of neural models in different RE scenarios .

All the code and datasets are released at https://github.com / thunlp/ RE-Context-or-Names .

Relation extraction ( RE ) aims at extracting relational facts between entities from text , e.g. , extracting the fact ( SpaceX , founded by , Elon Musk ) from the sentence in Figure 1 .

Utilizing the structured knowledge captured by RE , we can construct or complete knowledge graphs ( KGs ) , and eventually support downstream applications like question answering ( Bordes et al . , 2014 ) , dialog systems ( Madotto et al . , 2018 ) engines ( Xiong et al . , 2017 ) .

With the recent advance of deep learning , neural relation extraction ( NRE ) models ( Socher et al . , 2012 ; Liu et al . , 2013 ; Baldini Soares et al . , 2019 ) have achieved the latest state-of-the-art results and some of them are even comparable with human performance on several public RE benchmarks .

The success of NRE models on current RE benchmarks makes us wonder which type of information these models actually grasp to help them extract correct relations .

The analysis of this problem may indicate the nature of these models and reveal their remaining problems to be further explored .

Generally , in a typical RE setting , there are two main sources of information in text that might help RE models classify relations : textual context and entity mentions ( names ) .

From human intuition , textual context should be the main source of information for RE .

Researchers have reached a consensus that there exist interpretable patterns in textual context that express relational facts .

For example , in Figure 1 , " ... be founded ... by ... " is a pattern for the relation founded by .

The early RE systems ( Huffman , 1995 ; Califf and Mooney , 1997 ) formalize patterns into string templates and determine relations by matching these templates .

The later neural models ( Socher et al . , 2012 ; Liu et al . , 2013 ) prefer to encode patterns into distributed representations and then predict relations via representation matching .

Compared with rigid string templates , distributed representations used in neural models are more generalized and perform better .

Besides , entity mentions also provide much information for relation classification .

As shown in Figure 1 , we can acquire the types of entities from their mentions , which could help to filter out those impossible relations .

Besides , if these entities can be linked to KGs , models can introduce external knowledge from KGs to help RE ( Zhang et al . , 2019 ; Peters et al . , 2019 ) .

Moreover , for pre-trained language models , which are widely adopted for recent RE models , there may be knowledge about entities inherently stored in their parameters after pre-training ( Petroni et al . , 2019 ) .

In this paper , we carry out extensive experiments to study to what extent RE models rely on the two information sources .

( 1 ) Both context and entity mentions are crucial for RE .

As shown in our experiments , while context is the main source to support classification , entity mentions also provide critical information , most of which is the type information of entities .

( 2 ) Existing RE benchmarks may leak shallow cues via entity mentions , which contribute to the high performance of existing models .

Our experiments show that models still can achieve high performance only given entity mentions as input , suggesting that there exist biased statistical cues from entity mentions in these datasets .

The above observations demonstrate how existing models work on RE datasets , and suggest a way to further improve RE models : we should enhance them via better understanding context and utilizing entity types , while preventing them from simply memorizing entities or exploiting biased cues in mentions .

From these points , we investigate an entity-masked contrastive pre-training framework for RE .

We use Wikidata to gather sentences that may express the same relations , and let the model learn which sentences are close and which are not in relational semantics by a contrastive objective .

In this process , we randomly mask entity mentions to avoid being biased by them .

We show its effectiveness across several settings and benchmarks , and suggest that better pre-training technique is a reliable direction towards better RE .

In this paper , we thoroughly study how textual context and entity mentions affect RE models respectively .

Experiments and case studies prove that ( i ) both context and entity mentions ( mainly as type information ) provide critical information for relation extraction , and ( ii ) existing RE datasets may leak superficial cues through entity mentions and models may not have the strong abilities to understand context as we expect .

From these points , we propose an entity-masked contrastive pre-training framework for RE to better understand textual context and entity types , and experimental results prove the effectiveness of our method .

In the future , we will continue to explore better RE pre-training techniques , especially with a focus on open relation extraction and relation discovery .

These problems require models to encode good relational representation with limited or even zero annotations , and we believe that our pre-trained RE models will make a good impact in the area .


As a promising paradigm , interactive semantic parsing has shown to improve both semantic parsing accuracy and user confidence in the results .

In this paper , we propose a new , unified formulation of the interactive semantic parsing problem , where the goal is to design a modelbased intelligent agent .

The agent maintains its own state as the current predicted semantic parse , decides whether and where human intervention is needed , and generates a clarification question in natural language .

A key part of the agent is a world model : it takes a percept ( either an initial question or subsequent feedback from the user ) and transitions to a new state .

We then propose a simple yet remarkably effective instantiation of our framework , demonstrated on two text-to-SQL datasets ( WikiSQL and Spider ) with different state-of-the-art base semantic parsers .

Compared to an existing interactive semantic parsing approach that treats the base parser as a black box , our approach solicits less user feedback but yields higher run-time accuracy .

Natural language interfaces that allow users to query data and invoke services without programming have been identified as a key application of semantic parsing ( Berant et al . , 2013 ; Thomason et al . , 2015 ; Dong and Lapata , 2016 ; Zhong et al . , 2017 ; Campagna et al . , 2017 ; Su et al . , 2017 ) .

However , existing semantic parsing technologies often fall short when deployed in practice , facing several challenges : ( 1 ) user utterances can be inherently ambiguous or vague , making it difficult to get the correct result in one shot , ( 2 ) the accuracy of state-of-the-art semantic parsers are still not high enough for real use , and ( 3 ) it is hard for users to validate the semantic parsing results , especially with mainstream neural network models that are known for the lack of interpretability .

In response to these challenges , interactive semantic parsing has been proposed recently as a practical solution , which includes human users in the loop to resolve utterance ambiguity , boost system accuracy , and improve user confidence via human-machine collaboration ( Li and Jagadish , 2014 ; He et al . , 2016 ; Chaurasia and Mooney , 2017 ; Su et al . , 2018 ; Gur et al . , 2018 ; Yao et al . , 2019 ) .

For example , Gur et al . ( 2018 ) built the DialSQL system to detect errors in a generated SQL query and request user selection on alternative options via dialogues .

Similarly , Chaurasia and Mooney ( 2017 ) and Yao et al . ( 2019 ) enabled semantic parsers to ask users clarification questions while generating an If-Then program .

Su et al . ( 2018 ) showed that users overwhelmingly preferred an interactive system over the noninteractive counterpart for natural language interfaces to web APIs .

While these recent studies successfully demonstrated the value of interactive semantic parsing in practice , they are often bound to a certain type of formal language or dataset , and the designs are thus ad-hoc and not easily generalizable .

For example , DialSQL only applies to SQL queries on the WikiSQL dataset ( Zhong et al . , 2017 ) , and it is non-trivial to extend it to other formal languages ( e.g. , λ-calculus ) or even just to more complex SQL queries beyond the templates used to construct the dataset .

Aiming to develop a general principle for building interactive semantic parsing systems , in this work we propose model-based interactive semantic parsing ( MISP ) , where the goal is to design a model-based intelligent agent ( Russell and Norvig , 2009 ) that can interact with users to complete a semantic parsing task .

Taking an utterance ( e.g. , a natural language question ) as input , the agent forms the semantic parse ( e.g. , a SQL query ) in steps , potentially soliciting user feedback in some steps to correct parsing errors .

As illustrated in Figure 1 , a MISP agent maintains its state as the current semantic parse and , via an error detector , decides whether and where human intervention is needed ( the action ) .

This action is performed by a question generator ( the actuator ) , which generates and presents to the user a humanunderstandable question .

A core component of the agent is a world model ( Ha and Schmidhuber , 2018 )

( hence model-based ) , which incorporates user feedback from the environment and transitions to a new agent state ( e.g. , an updated semantic parse ) .

This process repeats until a terminal state is reached .

Such a design endows a MISP agent with three crucial properties of interactive semantic parsing : ( 1 ) being introspective of the reasoning process and knowing when it may need human supervision , ( 2 ) being able to solicit user feedback in a human-friendly way , and ( 3 ) being able to incorporate user feedback ( through state transitions controlled by the world model ) .

The MISP framework provides several advantages for designing an interactive semantic parser compared to the existing ad-hoc studies .

For instance , the whole problem is conceptually reduced to building three key components ( i.e. , the world model , the error detector , and the actuator ) , and can be handled and improved separately .

While each component may need to be tailored to the specific task , the general framework remains unchanged .

In addition , the formulation of a modelbased intelligent agent can facilitate the application of other machine learning techniques like reinforcement learning .

To better demonstrate the advantages of the MISP framework , we propose a simple yet re-markably effective instantiation for the text-to-SQL task .

We show the effectiveness of the framework based on three base semantic parsers ( SQLNet , SQLova and SyntaxSQLNet ) and two datasets ( WikiSQL and Spider ) .

We empirically verified that with a small amount of targeted , testtime user feedback , interactive semantic parsers improve the accuracy by 10 % to 15 % absolute .

Compared to an existing interactive semantic parsing system , DialSQL ( Gur et al . , 2018 ) , our approach , despite its much simpler yet more general system design , achieves better parsing accuracy by asking only half as many questions .

This work proposes a new and unified framework for the interactive semantic parsing task , named MISP , and instantiates it successfully on the textto-SQL task .

We outline several future directions to further improve MISP-SQL and develop MISP systems for other semantic parsing tasks :

The flexibility of MISP allows improving on each agent compo-nent separately .

One can augment the probability-based error detector in MISP-SQL with probability calibration , which has been shown useful in aligning model confidence with its reliability ( Guo et al . , 2017 ) .

One can also use learning-based approaches , such as a reinforced decision policy ( Yao et al . , 2019 ) , to increase the rate of identifying wrong and solvable predictions .

Lifelong Learning for Semantic Parsing .

Learning from user feedback is a promising solution for lifelong semantic parser improvement ( Iyer et al . , 2017 ; Padmakumar et al . , 2017 ; Labutov et al . , 2018 ) .

However , this may lead to a non-stationary environment ( e.g. , changing state transition ) from the perspective of the agent , making its training ( e.g. , error detector learning ) unstable .

In the context of dialog systems , Padmakumar et al . ( 2017 ) suggests that this effect can be mitigated by jointly updating the dialog policy and the semantic parser batchwisely .

We leave exploring this aspect in our task to future work .

It is important for MISP agents to scale to larger backend data sources ( e.g. , knowledge bases like Freebase or Wikidata ) .

To this end , one can improve MISP from at least three aspects :

( 1 ) using more intelligent interaction designs ( e.g. , free-form text as user feedback ) to speed up the hypothesis space searching globally , ( 2 ) strengthening the world model to nail down a smaller set of plausible hypotheses based on both the initial question and user feedback , and ( 3 ) training the agent to learn to improve the parsing accuracy while minimizing the number of required human interventions over time .


This paper proposes a method for detecting errors in article usage and singular

plural usage based on the mass count distinction .

First , it learns decision lists from training data generated automatically to distinguish mass and count nouns .

Then , in order to improve its performance , it is augmented by feedback that is obtained from the writing of learners .

Finally , it detects errors by applying rules to the mass count distinction .

Experiments show that it achieves a recall of 0.71 and a precision of 0.72 and outperforms other methods used for comparison when augmented by feedback .

Although several researchers ( Kawai et al . , 1984 ; McCoy et al . , 1996 ; Schneider and McCoy , 1998 ; Tschichold et al . , 1997 ) have shown that rulebased methods are effective to detecting grammatical errors in the writing of learners of English , it has been pointed out that it is hard to write rules for detecting errors concerning the articles and singular plural usage .

To be precise , it is hard to write rules for distinguishing mass and count nouns which are particularly important in detecting these errors ( Kawai et al . , 1984 ) .

The major reason for this is that whether a noun is a mass noun or a count noun greatly depends on its meaning or its surrounding context ( refer to Allan ( 1980 ) and Bond ( 2005 ) for details of the mass count distinction ) .

The above errors are very common among Japanese learners of English ( Kawai et al . , 1984 ; Izumi et al . , 2003 ) .

This is perhaps because the Japanese language does not have a mass count distinction system similar to that of English .

Thus , it is favorable for error detection systems aiming at Japanese learners to be capable of detecting these errors .

In other words , such systems need to somehow distinguish mass and count nouns .

This paper proposes a method for distinguishing mass and count nouns in context to complement the conventional rules for detecting grammatical errors .

In this method , first , training data , which consist of instances of mass and count nouns , are automatically generated from a corpus .

Then , decision lists for distinguishing mass and count nouns are learned from the training data .

Finally , the decision lists are used with the conventional rules to detect the target errors .

The proposed method requires a corpus to learn decision lists for distinguishing mass and count nouns .

General corpora such as newspaper articles can be used for the purpose .

However , a drawback to it is that there are differences in character between general corpora and the writing of non-native learners of English ( Granger , 1998 ; Chodorow and Leacock , 2000 ) .

For instance , Chodorow and Leacock ( 2000 ) point out that the word concentrate is usually used as a noun in a general corpus whereas it is a verb 91 % of the time in essays written by non-native learners of English .

Consequently , the differences affect the performance of the proposed method .

In order to reduce the drawback , the proposed method is augmented by feedback ; it takes as feedback learners ' essays whose errors are corrected by a teacher of English ( hereafter , referred to as the feedback corpus ) .

In essence , the feedback corpus could be added to a general corpus to generate training data .

Or , ideally training data could be generated only from the feedback corpus just as from a general corpus .

However , this causes a serious problem in practice since the size of the feedback corpus is normally far smaller than that of a general corpus .

To make it practical , this paper discusses the problem and explores its solution .

The rest of this paper is structured as follows .

Section 2 describes the method for detecting the target errors based on the mass count distinction .

Section 3 explains how the method is augmented by feedback .

Section 4 discusses experiments conducted to evaluate the proposed method .

This paper has proposed a feedback-augmented method for distinguishing mass and count nouns to complement the conventional rules for detecting grammatical errors .

The experiments have shown that the proposed method detected 71 % of the target errors in the writing of Japanese learners of English with a precision of 72 % when it was augmented by feedback .

From the results , we conclude that the feedback-augmented method is effective to detecting errors concerning the articles and singular plural usage in the writing of Japanese learners of English .

Although it is not taken into account in this paper , the feedback corpus contains further useful information .

For example , we can obtain training data consisting of instances of errors by comparing the feedback corpus with its original corpus .

Also , comparing it with the results of detection , we can know performance of each rule used in the detection , which make it possible to increase or decrease their log-likelihood ratios according to their performance .

We will investigate how to exploit these sources of information in future work .