Variational autoencoder-based text-to-speech systems encapsulate prosody information into latent representations lacking control over prosody at inference time that contribute to naturalness and expressiveness.

They propose to estimate a posterior probability distribution of latent prosody features conditioned on acoustic features, speaker information, and text features to generate expressive prosody.

The proposed system improves naturalness and prosody diversity over the FastSpeech baseline and two variants on a qualitative listening test with LJ-Speech and LibriTTS data.

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

Abstract: 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 .

Abstract: 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 .

Abstract: 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 .

Abstract: 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 .


While responses with images are important for conversational agents, existing methods only depend on retrieval-based methods but not generation, and obtaining multimodal dialogues is difficult.

They propose a multimodal task and an agent which can learn a response generator and text-to-image translator, and use a few samples to join them.

The proposed model achieves state-of-the-art results in automatic and human evaluation and can generate informative text and high-resolution images on PhotoChat Corpus.

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

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

Abstract: 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 .

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

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

Abstract: 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 .

Abstract: 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 .

Abstract: 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 .


Existing methods for opinion target expression recognition use supervised algorithms which require expensive manually annotated corpora unable to scale in languages and domains.

They propose to apply a zero-shot cross-lingual approach by training a convolutional neural network with multilingual word embeddings only on the source language.

The proposed approach achieves up to 77% of the model trained on the target language and 87% when trained on multiple source languages.

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

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

Abstract: 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 .

Abstract: 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 .

Abstract: 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 .

Abstract: 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 .

Abstract: 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 .

Abstract: 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 .


Which textual information models are used for relation extraction tasks remain unknown hindering to improvement of their performance.

They perform an analysis and use the findings to propose an entity-masked contrastive pretraining to avoid models memorizing entities or using biased cues in mentions.

They found that existing benchmarks leak superficial cues from entity mentions, and the proposed model can achieve robust performance without relying on these features.

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

Abstract: 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 .

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

Abstract: 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 .

Abstract: 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 .

Abstract: 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 .

Abstract: 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 .


Interactive semantic parsing systems can take user utterances to improve their outputs but are currently limited in languages or datasets due to their ad-hoc nature.

They propose to approach interactive semantic parsing with a mode-based agent interacting with users as needed to perform error-less parsing.

The proposed simple agent parser outperforms existing baseline parsers while keeping the interactions with users in half.

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

Abstract: 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 .

Abstract: 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 .

Abstract: 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 .

Abstract: 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 .

Abstract: 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 .


Although they are common, writing rules for detecting errors concerning the articles and singular plural usage especially for distinguishing mass and count nouns is hard.

They propose to complement conventional rules with decision lists learned from automatically generated training data and feedback obtained from the writing of learners.

The proposed method outperforms other methods by achieving a recall of 0.71 and a precision of 0.72 when augmented by feedback.

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

Abstract: plural usage based on the mass count distinction .

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

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

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

Abstract: 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 .


Sentiment classifiers suffer from out-of-domain inputs and labelled data for supervised domain adaption is not often available limiting the applicability of such a model.

They propose a cross-domain adaptation method which can map examples between source and target domains with their labels by a linear transformation.

The proposed model outperforms several baseline methods and is competitive with the state-of-the-art method on sentiment classification tasks from 4 types of Amazon products.

Abstract: Sentiment classification aims to automatically predict sentiment polarity ( e.g. , positive or negative ) of user generated sentiment data ( e.g. , reviews , blogs ) .

Abstract: Due to the mismatch among different domains , a sentiment classifier trained in one domain may not work well when directly applied to other domains .

Abstract: Thus , domain adaptation for sentiment classification algorithms are highly desirable to reduce the domain discrepancy and manual labeling costs .

Abstract: To address the above challenge , we propose a novel domain adaptation method , called Bi-Transferring Deep Neural Networks ( BTDNNs ) .

Abstract: The proposed BTDNNs attempts to transfer the source domain examples to the target domain , and also transfer the target domain examples to the source domain .

Abstract: The linear transformation of BTDNNs ensures the feasibility of transferring between domains , and the distribution consistency between the transferred domain and the desirable domain is constrained with a linear data reconstruction manner .

Abstract: As a result , the transferred source domain is supervised and follows similar distribution as the target domain .

Abstract: Therefore , any supervised method can be used on the transferred source domain to train a classifier for sentiment classification in a target domain .

Abstract: We conduct experiments on a benchmark composed of reviews of 4 types of Amazon products .

Abstract: Experimental results show that our proposed approach significantly outperforms the several baseline methods , and achieves an accuracy which is competitive with the state-of-the-art method for domain adaptation .

With the rise of social media ( e.g. , blogs and social networks etc . ) , more and more user generated sentiment data have been shared on the Web ( Pang et al . , 2002 ; Pang and Lee , 2008 ; Liu , 2012 ; Zhou et al . , 2011 ) .

They exist in the form of user reviews on shopping or opinion sites , in posts of blogs / questions or customer feedbacks .

This has created a surge of research in sentiment classification ( or sentiment analysis ) , which aims to automatically determine the sentiment polarity ( e.g. , positive or negative ) of user generated sentiment data ( e.g. , reviews , blogs , questions ) .

Machine learning algorithms have been proved promising and widely used for sentiment classification ( Pang et al . , 2002 ;

However , the performance of these models relies on manually labeled training data .

In many practical cases , we may have plentiful labeled data in the source domain , but very few or no labeled data in the target domain with a different data distribution .

For example , we may have many labeled books reviews , but we are interested in detecting the polarity of electronics reviews .

Reviews for different products might have different vocabularies , thus classifiers trained on one domain often fail to produce satisfactory results when transferring to another domain .

This has motivated much research on cross-domain ( domain adaptation ) sentiment classification which transfers the knowledge from the source domain to the target domain ( Thomas et al . , 2006 ; Snyder and Barzilay , 2007 ; Blitzer et al . , 2006 ; Blitzer et al . , 2007 ; Daume III , 2007 ; Li and Zong , 2008 ; Li et al . , 2009 ; Pan et al . , 2010 ; Kumar et al . , 2010 ; Glorot et al . , 2011 ; Chen et al . , 2011a ; Chen et al . , 2012 ; Li et al . , 2012 ; Xia et al . , 2013a ; Li et al . , 2013 ; Zhou et al . , 2015a ; Zhuang et al . , 2015 ) .

Depending on whether the labeled data are available for the target domain , cross-domain sen-timent classification can be divided into two categories : supervised domain adaptation and unsupervised domain adaptation .

In scenario of supervised domain adaptation , labeled data is available in the target domain but the number is usually too small to train a good sentiment classifier , while in unsupervised domain adaptation only unlabeled data is available in the target domain , which is more challenging .

This work focuses on the unsupervised domain adaptation problem of which the essence is how to employ the unlabeled data of target domain to guide the model learning from the labeled source domain .

The fundamental challenge of cross-domain sentiment classification lies in that the source domain and the target domain have different data distribution .

Recent work has investigated several techniques for alleviating the domain discrepancy : instance-weight adaptation ( Huang et al . , 2007 ; Jiang and Zhai , 2007 ; Li and Zong , 2008 ; Mansour et al . , 2009 ; Dredze et al . , 2010 ; Chen et al . , 2011b ; Chen et al . , 2011a ; Chen et al . , 2012 ; Li et al . , 2013 ; Xia et al . , 2013a ) and feature representation adaptation ( Thomas et al . , 2006 ; Snyder and Barzilay , 2007 ; Blitzer et al . , 2006 ; Blitzer et al . , 2007 ; Li et al . , 2009 ; Pan et al . , 2010 ; Zhou et al . , 2015a ; Zhuang et al . , 2015 ) .

The first kind of methods assume that some training data in the source domain are very useful for the target domain and these data can be used to train models for the target domain after re-weighting .

In contrast , feature representation approaches attempt to develop an adaptive feature representation that is effective in reducing the difference between domains .

Recently , some efforts have been initiated on learning robust feature representations with deep neural networks ( DNNs ) in the context of crossdomain sentiment classification ( Glorot et al . , 2011 ; Chen et al . , 2012 ) .

Glorot et al . ( 2011 ) proposed to learn robust feature representations with stacked denoising auto-encoders ( SDAs ) ( Vincent et al . , 2008 ) .

Denoising auto-encoders are onelayer neural networks that are optimized to reconstruct input data from partial and random corruption .

These denoisers can be stacked into deep learning architectures .

The outputs of their intermediate layers are then used as input features for SVMs ( Fan et al . , 2008 ) .

Chen et al . ( 2012 ) proposed a marginalized SDA ( mSDA ) that addressed the two crucial limitations of SDAs : high computational cost and lack of scalability to highdimensional features .

However , these methods learn the unified domain-invariable feature representations by combining the source domain data and that of the target domain data together , which can not well characterize the domain-specific features as well as the commonality of domains .

To this end , we propose a Bi-Transferring Deep Neural Networks ( BTDNNs ) which can transfer the source domain examples to the target domain and also transfer the target domain examples to the source domain , as shown in Figure 1 .

In BTDNNs , the linear transformation makes the feasibility of transferring between domains , and the linear data reconstruction manner ensures the distribution consistency between the transferred domain and the desirable domain .

Specifically , our BTDNNs has one common encoder f c , two decoders g s and g t which can map an example to the source domain and the target domain respectively .

As a result , the source domain can be transferred to the target domain along with its sentiment label , and any supervised method can be used on the transferred source domain to train a classifier for sentiment classification in the target domain , as the transferred source domain data share the similar distribution as the target domain .

Experimental results show that the proposed approach significantly outperforms several baselines , and achieves an accuracy which is competitive with the state-of-the-art method for cross-domain sentiment classification .

The remainder of this paper is organized as follows .

Section 3 describes our proposed bi-transferring deep neural networks ( BTDNNs ) .

Section 4 presents the experimental results .

In Section 5 , we conclude with ideas for future research .

In this paper , we propose a novel Bi-Transferring Deep Neural Networks ( BTDNNs ) for crossdomain sentiment classification .

Experimental results show that BTDNNs significantly outperforms the several baselines , and achieves an accuracy which is competitive with the state-of-the-art method for sentiment classification adaptation .

There are some ways in which this research could be continued .

First , since deep learning may obtain better generalization on large-scale data sets ( Bengio , 2009 ) , a straightforward path of the future research is to apply the proposed BTDNNs for domain adaptation on a much larger industrial-strength data set of 22 domains ( Glorot et al . , 2011 ) .

Second , we will try to investigate the use of the proposed approach for other kinds of data set , such as 20 newsgroups and Reuters-21578 ( Li et al . , 2012 ; Zhuang et al . , 2013 ) .


Existing models that estimate dependency likelihood for Japanese use hand-crafted rules which have few problems such as low coverage, consistency, and domain scalability.

They propose to use a maximum entropy model which leans weights from a training corpus using human-defined features of bunsetsu, Japanese phrasal units.

The proposed system achieves 87.2% accuracy on the Kyoto University corpus, and they find that basic and combined features contribute to improvements.

Abstract: This paper describes a dependency structure analysis of Japanese sentences based on the maximum entropy models .

Abstract: Our model is created by learning the weights of some features from a training corpus to predict the dependency between bunsetsus or phrasal units .

Abstract: The dependency accuracy of our system is 87.2 % using the Kyoto University corpus .

Abstract: We discuss the contribution of each feature set and the relationship between the number of training data and the accuracy .

Dependency structure analysis is one of the basic techniques in Japanese sentence analysis .

The Japanese dependency structure is usually represented by the relationship between phrasal units called ' bunsetsu . '

In the first step , a dependency matrix is prepared .

Each element of the matrix represents how likely one bunsetsu is to depend on the other .

In the second step , an optimal set of dependencies for the entire sentence is found .

In this paper , we will mainly discuss the first step , a model for estimating dependency likelihood .

So far there have been two different approaches to estimating the dependency likelihood , One is the rule-based approach , in which the rules are created by experts and likelihoods are calculated by some means , including semiautomatic corpusbased methods but also by manual assignment of scores for rules .

However , hand-crafted rules have the following problems .

• They have a problem with their coverage .

Because there are many features to find correct dependencies , it is difficult to find them manually .

They also have a problem with their consistency , since many of the features compete with each other and humans can not create consistent rules or assign consistent scores .

As syntactic characteristics differ across different domains , the rules have to be changed when the target domain changes .

It is costly to create a new hand-made rule for each domain .

At / other approach is a fully automatic corpusbased approach .

This approach has the potential to overcome the problems of the rule-based approach .

It automatically learns the likelihoods of dependencies from a tagged corpus and calculates the best dependencies for an input sentence .

This approach is taken by some other systems ( Collins , 1996 ; Fujio and Matsumoto , 1998 ; Haruno et ah , 1998 ) .

The parser proposed by Ratnaparkhi ( Ratnaparkhi , 1997 ) is considered to be one of the most accurate parsers in English .

Its probability estimation is based on the maximum entropy models .

This model learns the weights of given features from a training corpus .

The weights are calculated based on the frequencies of the features in the training data .

The set of features is defined by a human .

In our model , we use features of bunsetsu , such as character strings , parts of speech , and inflection types of bunsetsu , as well as information between bunsetsus , such as the existence of punctuation , and the distance between bunsetsus .

The probabilities of dependencies are estimated from the model by using those features in input sentences .

We assume that the overall dependencies in a whole sentence can be determined as the product of the probabilities of all the dependencies in the sentence .

Now , we briefly describe the algorithm of dependency analysis .

It is said that Japanese dependencies have the following characteristics .

( 1 ) Dependencies are directed from left to right ( 2 ) Dependencies do not cross ( 3 ) A bunsetsu , except for the rightmost one , depends on only one bunsetsu ( 4 ) In many cases , the left context is not necessary to determine a dependency 1

The analysis method proposed in this paper is designed to utilize these features .

Based on these properties , we detect the dependencies in a sentence by analyzing it backwards ( from right to left ) .

In the past , such a backward algorithm has been used with rule-based parsers ( e.g. , ( Fujita , 1988 ) ) .

We applied it to our statistically based approach .

Because of the statistical property , we can incorporate a beam search , an effective way of limiting the search space in a backward analysis .

This paper described a Japanese dependency structure analysis based on the maximum entropy model .

The probabilities of dependencies between bunsetsus are estimated by this model .

The dependency accuracy of our system was 87.2 % using the Kyoto University corpus .

In our experiments without the feature sets shown in Tables 1 and 2 , we found that some basic and combined features strongly contribute to improve the accuracy .

Investigating the relationship between the number of training data and the accuracy , we found that good accuracy can be achieved even with a very small set of training data .

We believe that the maximum entropy framework has suitable characteristics for overcoming the data sparseness problem .

In particular , we are interested in how to deal with coordinate structures , since that seems to be the largest problem at the moment .


Existing approaches to replace the over-simplified objective for open domain dialogue generation use manually defined reward functions which cannot cover all crucial aspects.

They propose the Turing test-inspired adversarial training scheme where the generative model is trained to fool the discriminator as a reinforcement learning problem.

The proposed model can generate more interactive, interesting, and non-repetitive responses than previous baselines on several metrics including adversarial evaluation.

Abstract: In this paper , drawing intuition from the Turing test , we propose using adversarial training for open-domain dialogue generation : the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances .

Abstract: We cast the task as a reinforcement learning ( RL ) problem where we jointly train two systems , a generative model to produce response sequences , and a discriminator-analagous to the human evaluator in the Turing test-to distinguish between the human-generated dialogues and the machine-generated ones .

Abstract: The outputs from the discriminator are then used as rewards for the generative model , pushing the system to generate dialogues that mostly resemble human dialogues .

Abstract: In addition to adversarial training we describe a model for adversarial evaluation that uses success in fooling an adversary as a dialogue evaluation metric , while avoiding a number of potential pitfalls .

Abstract: Experimental results on several metrics , including adversarial evaluation , demonstrate that the adversarially-trained system generates higher-quality responses than previous baselines .

Open domain dialogue generation ( Ritter et al . , 2011 ; Sordoni et al . , 2015 ; Xu et al . , 2016 ; Wen et al . , 2016 ; Li et al . , 2016b ; Serban et al . , 2016c Serban et al . , , 2017 ) ) aims at generating meaningful and coherent dialogue responses given the dialogue history .

Prior systems , e.g. , phrase-based machine translation systems ( Ritter et al . , 2011 ; Sordoni et al . , 2015 ) or end-to-end neural systems ( Shang et al . , 2015 ; Vinyals and Le , 2015 ; Li et al . , 2016a ; Yao et al . , 2015 ; Luan et al . , 2016 ) approximate such a goal by predicting the next dialogue utterance given the dialogue history using the maximum likelihood estimation ( MLE ) objective .

Despite its success , this over-simplified training objective leads to problems : responses are dull , generic ( Sordoni et al . , 2015 ; Serban et al . , 2016a ; Li et al . , 2016a ) , repetitive , and short-sighted ( Li et al . , 2016d ) .

Solutions to these problems require answering a few fundamental questions : what are the crucial aspects that characterize an ideal conversation , how can we quantitatively measure them , and how can we incorporate them into a machine learning system ?

For example , Li et al . ( 2016d ) manually define three ideal dialogue properties ( ease of answering , informativeness and coherence ) and use a reinforcement-learning framework to train the model to generate highly rewarded responses .

Yu et al . ( 2016b ) use keyword retrieval confidence as a reward .

However , it is widely acknowledged that manually defined reward functions ca n't possibly cover all crucial aspects and can lead to suboptimal generated utterances .

A good dialogue model should generate utterances indistinguishable from human dialogues .

Such a goal suggests a training objective resembling the idea of the Turing test ( Turing , 1950 ) .

We borrow the idea of adversarial training ( Goodfellow et al . , 2014 ; Denton et al . , 2015 ) in computer vision , in which we jointly train two models , a generator ( a neural SEQ2SEQ model ) that defines the probability of generating a dialogue sequence , and a discriminator that labels dialogues as human-generated or machine-generated .

This discriminator is analogous to the evaluator in the Turing test .

We cast the task as a reinforcement learning problem , in which the quality of machinegenerated utterances is measured by its ability to fool the discriminator into believing that it is a human-generated one .

The output from the discriminator is used as a reward to the generator , pushing it to generate utterances indistinguishable from human-generated dialogues .

The idea of a Turing test-employing an evaluator to distinguish machine-generated texts from human-generated ones-can be applied not only to training but also testing , where it goes by the name of adversarial evaluation .

Adversarial evaluation was first employed in Bowman et al . ( 2016 ) to evaluate sentence generation quality , and preliminarily studied for dialogue generation by Kannan and Vinyals ( 2016 ) .

In this paper , we discuss potential pitfalls of adversarial evaluations and necessary steps to avoid them and make evaluation reliable .

Experimental results demonstrate that our approach produces more interactive , interesting , and non-repetitive responses than standard SEQ2SEQ models trained using the MLE objective function .

In this paper , drawing intuitions from the Turing test , we propose using an adversarial training approach for response generation .

We cast the model in the framework of reinforcement learning and train a generator based on the signal from a discriminator to generate response sequences indistinguishable from human-generated dialogues .

We observe clear performance improvements on multiple metrics from the adversarial training strategy .

The adversarial training model should theoretically benefit a variety of generation tasks in NLP .

Unfortunately , in preliminary experiments applying the same training paradigm to machine translation , we did not observe a clear performance boost .

We conjecture that this is because the adversarial training strategy is more beneficial to tasks in which there is a big discrepancy between the distributions of the generated sequences and the reference target sequences .

In other words , the adversarial approach is more beneficial on tasks in which entropy of the targets is high .

Exploring this relationship further is a focus of our future work .

Zhou Yu , Ziyu Xu , Alan W Black , and Alex I Rudnicky . 2016b .

Strategy and policy learning for nontask-oriented conversational systems .

In 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue .

Yuan Zhang , Regina Barzilay , and Tommi Jaakkola . 2017 .

Aspect-augmented adversarial networks for domain adaptation .


Because users provide natural language texts to the search engine, choosing the right segmentations helps for faster retrieval, and when original queries return insufficient pages.

They create a manually-segmented user query dataset and propose a classification-based query segmentation system which uses features such as token associations beyond simple N-gram counts.

The proposed system achieves 86% segmentation-decision accuracy on a set of segmented noun phrase queries which is 56% fewer errors than a comparison approach.

Abstract: Query segmentation is the process of taking a user 's search-engine query and dividing the tokens into individual phrases or semantic units .

Abstract: Identification of these query segments can potentially improve both document-retrieval precision , by first returning pages which contain the exact query segments , and document-retrieval recall , by allowing query expansion or substitution via the segmented units .

Abstract: We train and evaluate a machine-learned query segmentation system that achieves 86 % segmentationdecision accuracy on a gold standard set of segmented noun phrase queries , well above recently published approaches .

Abstract: Key enablers of this high performance are features derived from previous natural language processing work in noun compound bracketing .

Abstract: For example , token association features beyond simple N-gram counts provide powerful indicators of segmentation .

Billions of times every day , people around the world communicate with Internet search engines via a small text box on a web page .

The user provides a sequence of words to the search engine , and the search engine interprets the query and tries to return web pages that not only contain the query tokens , but that are also somehow about the topic or idea that the query terms describe .

Recent years have seen a widespread recognition that the user is indeed providing natural language text to the search engine ; query tokens are not independent , unordered symbols to be matched on a web document but rather ordered words and phrases with syntactic relationships .

For example , Zhai ( 1997 ) pointed out that indexing on single-word symbols is not able to distinguish a search for " bank terminology " from one for " terminology bank . "

The reader can submit these queries to a current search engine to confirm that modern indexing does recognize the effect of token order on query meaning in some way .

Accurately interpreting query semantics also depends on establishing relationships between the query tokens .

For example , consider the query " two man power saw . "

There are a number of possible interpretations of this query , and these can be expressed through a number of different segmentations or bracketings of the query terms : One simple way to make use of these interpretations in search would be to put quotation marks around the phrasal segments to require the search engine to only find pages with exact phrase matches .

If , as seems likely , the searcher is seeking pages about the large , mechanically-powered two-man saws used by lumberjacks and sawyers to cut big trees , then the first segmentation is correct .

Indeed , a phrasal search for " two man power saw " on Google does find the device of interest .

So does the second interpretation , but along with other , less-relevant pages discussing competitions involving " two-man handsaw , two-woman handsaw , power saw log bucking , etc . "

The top document returned for the third interpretation , meanwhile , describes a man on a rampage at a subway station with two cordless power saws , while the fourth interpretation finds pages about topics ranging from hockey 's thrilling two-man power play advantage to the man power situation during the Second World War .

Clearly , choosing the right segmentation means finding the right documents faster .

Query segmentation can also help if insufficient pages are returned for the original query .

A technique such as query substitution or expansion ( Jones et al . , 2006 ) can be employed using the segmented units .

For example , we could replace the sexist " two man " modifier with the politically-correct " two person " phrase in order to find additional relevant documents .

Without segmentation , expanding via the individual words " two , " " man , " " power , " or " saw " could produce less sensible results .

In this paper , we propose a data-driven , machinelearned approach to query segmentation .

Similar to previous segmentation approaches described in Section 2 , we make a decision to segment or not to segment between each pair of tokens in the query .

Unlike previous work , we view this as a classification task where the decision parameters are learned discriminatively from gold standard data .

In Section 3 , we describe our approach and the features we use .

Section 4 describes our labelled data , as well as the specific tools used for our experiments .

Section 5 provides the results of our evaluation , and shows the strong gains in performance possible using a wide set of features within a discriminative framework .

We have developed a novel approach to search query segmentation and evaluated this approach on actual user queries , reducing error by 56 % over a recent comparison approach .

Gains in performance were made possible by both leveraging recent progress in feature engineering for noun compound bracketing , as well as using a flexible , discriminative incorporation of association information , beyond the decisionboundary tokens .

We have created and made available a set of manually-segmented user queries , and thus provided a new testing platform for other researchers in this area .

Our initial formulation of query segmentation as a structured learning problem , and our leveraging of association statistics beyond the decision boundary , also provides powerful tools for noun compound bracketing researchers to both move beyond three-word compounds and to adopt discriminative feature weighting techniques .

The positive results achieved on this important application should encourage further inter-disciplinary collaboration between noun compound interpretation and information retrieval researchers .

For example , analysing the semantics of multiword expressions may allow for more-focused query expansion ; knowing to expand " bank manager " to include pages describing a " manager of the bank , " but not doing the same for non-compositional phrases like " real estate " or " private investigator , " requires exactly the kind of techniques being developed in the noun compound interpretation community .

Thus for query expansion , as for query segmentation , work in natural language processing has the potential to make a real and immediate impact on search-engine technology .

The next step in this research is to directly investigate how query segmentation affects search performance .

For such an evaluation , we would need to know , for each possible segmentation ( including no segmentation ) , the document retrieval performance .

This could be the proportion of returned documents that are deemed to be relevant to the original query .

Exactly such an evaluation was recently used by Kumaran and Allan ( 2007 ) for the related task of query contraction .

Of course , a dataset with queries and retrieval scores may serve for more than evaluation ; it may provide the examples used by the learning module .

That is , the parameters of the contraction or segmentation scoring function could be discriminatively set to optimize the retrieval of the training set queries .

A unified framework for query contraction , segmentation , and expansion , all based on discriminatively optimizing retrieval performance , is a very appealing future research direction .

In this framework , the size of the training sets would not be limited by human annotation resources , but by the number of queries for which retrieved-document relevance judgments are available .

Generating more training examples would allow the use of more powerful , finer-grained lexical features for classification .


Non-traditional-spelling conventions in SMS messages require a normalization process before an NLP process can be applied but the current best system performs poorly.

They propose a machine translation-like corpus-based model surrounded by two spell checking-like modules composed of unambiguous token detection and non-alphabetic sequence identification.

The proposed method efficiently achieves a 9.3% Word Error Rate and 0.83 BLEU score in French by 10-fold-cross validation.

Abstract: In recent years , research in natural language processing has increasingly focused on normalizing SMS messages .

Abstract: Different well-defined approaches have been proposed , but the problem remains far from being solved : best systems achieve a 11 % Word Error Rate .

Abstract: This paper presents a method that shares similarities with both spell checking and machine translation approaches .

Abstract: The normalization part of the system is entirely based on models trained from a corpus .

Abstract: Evaluated in French by 10-fold-cross validation , the system achieves a 9.3 % Word Error Rate and a 0.83 BLEU score .

Introduced a few years ago , Short Message Service ( SMS ) offers the possibility of exchanging written messages between mobile phones .

These messages often greatly deviate from traditional spelling conventions .

As shown by specialists ( Thurlow and Brown , 2003 ; Fairon et al . , 2006 ; Bieswanger , 2007 ) , this variability is due to the simultaneous use of numerous coding strategies , like phonetic plays ( 2m1 read ' demain ' , " tomorrow " ) , phonetic transcriptions ( kom instead of ' comme ' , " like " ) , consonant skeletons ( tjrs for ' toujours ' , " always " ) , misapplied , missing or incorrect separators ( j esper for ' j'espère ' , " I hope " ; j'croibi1k , instead of ' je crois bien que ' , " I am pretty sure that " ) , etc .

These deviations are due to three main factors : the small number of characters allowed per text message by the service ( 140 bytes ) , the constraints of the small phones ' keypads and , last but not least , the fact that people mostly communicate between friends and relatives in an informal register .

Whatever their causes , these deviations considerably hamper any standard natural language processing ( NLP ) system , which stumbles against so many Out-Of-Vocabulary words .

For this reason , as noted by Sproat et al . ( 2001 ) , an SMS normalization must be performed before a more conventional NLP process can be applied .

As defined by Yvon ( 2008 ) , " SMS normalization consists in rewriting an SMS text using a more conventional spelling , in order to make it more readable for a human or for a machine . "

The SMS normalization we present here was developed in the general framework of an SMSto-speech synthesis system1 .

This paper , however , only focuses on the normalization process .

Evaluated in French , our method shares similarities with both spell checking and machine translation .

The machine translation-like module of the system performs the true normalization task .

It is entirely based on models learned from an SMS corpus and its transcription , aligned at the character-level in order to get parallel corpora .

Two spell checking-like modules surround the normalization module .

The first one detects unambiguous tokens , like URLs or phone numbers , to keep them out of the normalization .

The second one , applied on the normalized parts only , identifies non-alphabetic sequences , like punctuations , and labels them with the corresponding token .

This greatly helps the system 's print module to follow the basic rules of typography .

Section 2 proposes an overview of the state of the art .

Section 3 presents the general architecture of our system , while Section 4 focuses on how we learn and combine our normalization models .

Section 5 evaluates the system and compares it to previous works .

Section 6 draws conclusions and considers some future possible improvements of the method .

In this paper , we presented an SMS normalization framework based on finite-state machines and developed in the context of an SMS-to-speech synthesis system .

With the intention to avoid wrong modifications of special tokens and to handle word boundaries as easily as possible , we designed a method that shares similarities with both spell checking and machine translation .

Our Evaluated by ten-fold cross-validation , the system seems efficient , and the performance in terms of BLEU score and WER are quite encouraging .

However , the SER remains too high , which emphasizes the fact that the system needs several improvements .

First of all , the model should take phonetic similarities into account , because SMS messages contain a lot of phonetic plays .

The phonetic model , for instance , should know that o , au , eau , . . .

, aux can all be pronounced [ o ] , while è , ais , ait , . . .

However , unlike Kobus et al . ( 2008a ) , we feel that this model must avoid the normalization step in which the graphemic sequence is converted into phonemes , because this conversion prevents the next steps from knowing which graphemes were in the initial sequence .

Instead , we propose to learn phonetic similarities from a dictionary of words with phonemic transcriptions , and to build graphemes-to-graphemes rules .

These rules could then be automatically weighted , by learning their frequencies from our aligned corpora .

Furthermore , this model should be able to allow for timbre variation , like [ e]-[E ] , in order to allow similarities between graphemes frequently confused in French , like ai ( [ e ] ) and ais / ait / aient ( [ E ] ) .

Last but not least , the graphemes-tographemes rules should be contextualized , in order to reduce the complexity of the model .

It would also be interesting to test the impact of another lexical language model , learned on non-SMS sentences .

Indeed , the lexical model must be learned from sequences of standard written forms , an obvious prerequisite that involves a major drawback when the corpus is made of SMS sentences : the corpus must first be transcribed , an expensive process that reduces the amount of data on which the model will be trained .

For this reason , we propose to learn a lexical model from non-SMS sentences .

However , the corpus of external sentences should still share two important features with the SMS language : it should mimic the oral language and be as spontaneous as possible .

With this in mind , our intention is to gather sentences from Internet forums .

But not just any forum , because often forums share another feature with the SMS language : their language is noisy .

Thus , the idea is to choose a forum asking its members to pay attention to spelling mistakes and grammatical errors , and to avoid the use of the SMS language .


Beam search does not reflect the interactions between candidates leading to high overlaps while there are use-cases that call for multiple solutions.

They propose determinantal beam search which models over sets while encoding intra-set interactions to generate subsets with high diversity and coverage.

The proposed algorithm performs competitively in BLEU to baseline methods including the stochastic beam search on the machine translation task while optimizing for diversity.

Abstract: Beam search is a go-to strategy for decoding neural sequence models .

Abstract: The algorithm can naturally be viewed as a subset optimization problem , albeit one where the corresponding set function does not reflect interactions between candidates .

Abstract: Empirically , this leads to sets often exhibiting high overlap , e.g. , strings may differ by only a single word .

Abstract: Yet in use-cases that call for multiple solutions , a diverse or representative set is often desired .

Abstract: To address this issue , we propose a reformulation of beam search , which we call determinantal beam search .

Abstract: Determinantal beam search has a natural relationship to determinantal point processes ( DPPs ) , models over sets that inherently encode intra-set interactions .

Abstract: By posing iterations in beam search as a series of subdeterminant maximization problems , we can turn the algorithm into a diverse subset selection process .

Abstract: In a case study , we use the string subsequence kernel to explicitly encourage n-gram coverage in text generated from a sequence model .

Abstract: We observe that our algorithm offers competitive performance against other diverse set generation strategies in the context of language generation , while providing a more general approach to optimizing for diversity .

The decoding of neural sequence models is a fundamental component of many tasks in NLP .

Yet , many proposed decoding methods aim to produce only a single solution ; further , decoding strategies that provide a set , such as beam search , admit high overlap between solutions .

Such approaches fail to reflect that for many NLP tasks,1 there can be multiple correct solutions-or that we may desire a diverse set of solutions .

As it stands , standard beam search chooses items based purely on individual scores , with no means for encoding interaction between candidates ; this is the limitation which we attempt to address in this work .

We derive determinantal beam search , a novel generalization of beam search that casts subset selection as the subdeterminant optimization problem .

Specifically , we formulate each iteration of beam search as a subdeterminant maximization problem parameterized by a positive semi-definite matrix that encodes interactions between the possible candidates ; standard beam search is recovered by a specific diagonal matrix .

This framing creates a natural paradigm for taking the relationships between candidates during the decoding process , and can thus assign higher scores to diversified sets ; we show how this approach relates to k-determinantal point processes ( DPPs ) .

Given the wealth of research on efficient kernel computation ( Rousu and Shawe-Taylor , 2005 ; Farhan et al . , 2017 ) and DPP inference strategies ( Li et al . , 2016 ; Han et al . , 2017 ; Chen et al . , 2018 ) , we find the impact on runtime to be quite reasonable in comparison to standard decoding techniques .

In a case study on neural machine translation ( NMT ) , we demonstrate how to make use of the string subsequence kernel ( Lodhi et al . , 2002 ) to encode the notion of n-gram diversity in the language generation process , allowing us to derive an elegant diverse beam search .

Under this scheme , we observe that determinantal beam search generates more diverse sets than standard beam search with minimal trade-off in terms of BLEU .

We see improved performance over stochastic beam search ( SBS ; Kool et al . , 2019 ) , which is reported to encourage diversity , and a slight improvement over Vijayakumar et al . ( 2018 ) 's diverse beam search ( DBS ) while providing a more general approach to optimizing for intra-set diversity .

We propose determinantal beam search ( DetBS ): a new way of framing beam search that allows us to optimize set generation for diversity and coverage rather than simply individual scores .

Formally , we redefine beam search as an iterative subdeterminant maximization problems where we select the approximately maximizing set according to the PSD matrix parameterizing our score function .

This gives us the ability to encode the notion of intra-set diversity into the beam search optimization problem .

We discuss and experiment with efficient methods for inference and kernel computation that make DetBS an efficient decoding strategy in practice .

We use DetBS in the context of language generation , where we explicitly encourage n-gram coverage through the string subsequence kernel .

In our NMT experiments , we find DetBS generates much more diverse sets of strings than standard beam search and stochastic beam search with a small tradeoff in median BLEU .

We observe competitive performance compared with diverse beam search .


Increase of the number of informal textual communication such as email or instant messaging cacophony for users.

They propose a computational approach to the referent of named mentions in the body of an email by decomposing it into three different modules.

Experiments with three small and one large datasets show that the proposed approach performs on par with existing models and scales well.

Abstract: This paper describes a computational approach to resolving the true referent of a named mention of a person in the body of an email .

Abstract: A generative model of mention generation is used to guide mention resolution .

Abstract: Results on three relatively small collections indicate that the accuracy of this approach compares favorably to the best known techniques , and results on the full CMU Enron collection indicate that it scales well to larger collections .

The increasing prevalence of informal text from which a dialog structure can be reconstructed ( e.g. , email or instant messaging ) , raises new challenges if we are to help users make sense of this cacophony .

Large collections offer greater scope for assembling evidence to help with that task , but they pose additional challenges as well .

With well over 100,000 unique email addresses in the CMU version of the Enron collection ( Klimt and Yang , 2004 ) , common names ( e.g. , John ) might easily refer to any one of several hundred people .

In this paper , we associate named mentions in unstructured text ( i.e. , the body of an email and/or the subject line ) to modeled identities .

We see at least two direct applications for this work : ( 1 ) helping searchers who are unfamiliar with the contents of an email collection ( e.g. , historians or lawyers ) better understand the context of emails that they find , and ( 2 ) augmenting more typical social networks ( based on senders and recipients ) with additional links based on references found in unstructured text .

Most approaches to resolving identity can be decomposed into four sub-problems : ( 1 ) finding a reference that requires resolution , ( 2 ) identifying candidates , ( 3 ) assembling evidence , and ( 4 ) choosing among the candidates based on the evidence .

For the work reported in this paper , we rely on the user to designate references requiring resolution ( which we model as a predetermined set of mention-queries for which the correct referent is known ) .

Candidate identification is a computational expedient that permits the evidence assembly effort to be efficiently focused ; we use only simple techniques for that task .

Our principal contributions are the approaches we take to evidence generation ( leveraging three ways of linking to other emails where evidence might be found : reply chains , social interaction , and topical similarity ) and our approach to choosing among candidates ( based on a generative model of reference production ) .

We evaluate the effectiveness of our approach on four collections , three of which have previously reported results for comparison , and one that is considerably larger than the others .

The remainder of this paper is as follows .

Section 3 then describes our approach to modeling identity and ranking candidates .

Section 4 presents results , and Section 5 concludes .

We have presented an approach to mention resolution in email that flexibly makes use of expanding contexts to accurately resolve the identity of a given mention .

Our approach focuses on four naturally occurring contexts in email , including a message , a thread , other emails with senders and/or recipients in common , and other emails with significant topical content in common .

Our approach outperforms previously reported techniques and it scales well to larger collections .

Moreover , our results serve to highlight the importance of social context when resolving mentions in social media , which is an idea that deserves more attention generally .

In future work , we plan to extend our test collection with mention queries that must be resolved in the " long tail " of the identity distribution where less evidence is available .

We are also interested in exploring iterative approaches to jointly resolving mentions .


Despite the high performance, recursive neural networks requiring an external topological structure limit their applications and also quadraticly increase the computational costs with text lengths.

They propose a gated recursive neural network that uses a full binary tree to model recursive structures and also gating mechanisms for more complex modelling.

They show that the proposed system model has more complex structures on classification tasks from the Stanford Sentiment Treebank dataset.

Abstract: Recently , neural network based sentence modeling methods have achieved great progress .

Abstract: Among these methods , the recursive neural networks ( RecNNs ) can effectively model the combination of the words in sentence .

Abstract: However , RecNNs need a given external topological structure , like syntactic tree .

Abstract: In this paper , we propose a gated recursive neural network ( GRNN ) to model sentences , which employs a full binary tree ( FBT ) structure to control the combinations in recursive structure .

Abstract: By introducing two kinds of gates , our model can better model the complicated combinations of features .

Abstract: Experiments on three text classification datasets show the effectiveness of our model .

Recently , neural network based sentence modeling approaches have been increasingly focused on for their ability to minimize the efforts in feature engineering , such as Neural Bag-of-Words ( NBoW ) , Recurrent Neural Network ( RNN ) ( Mikolov et al . , 2010 ) , Recursive Neural Network ( RecNN ) ( Pollack , 1990 ; Socher et al . , 2013b ; Socher et al . , 2012 ) and Convolutional Neural Network ( CNN ) ( Kalchbrenner et al . , 2014 ; Hu et al . , 2014 ) .

Among these methods , recursive neural networks ( RecNNs ) have shown their excellent abilities to model the word combinations in sentence .

However , RecNNs require a pre-defined topological structure , like parse tree , to encode sentence , which limits the scope of its application .

Cho et al . ( 2014 ) proposed the gated recursive convolutional neural network ( grConv ) by utilizing the directed acyclic graph ( DAG ) structure instead of parse tree *

Left is a GRNN using a directed acyclic graph ( DAG ) structure .

Right is a GRNN using a full binary tree ( FBT ) structure .

( The green nodes , gray nodes and white nodes illustrate the positive , negative and neutral sentiments respectively . )

However , DAG structure is relatively complicated .

The number of the hidden neurons quadraticly increases with the length of sentences so that grConv can not effectively deal with long sentences .

Inspired by grConv , we propose a gated recursive neural network ( GRNN ) for sentence modeling .

Different with grConv , we use the full binary tree ( FBT ) as the topological structure to recursively model the word combinations , as shown in Figure 1 .

The number of the hidden neurons linearly increases with the length of sentences .

Another difference is that we introduce two kinds of gates , reset and update gates ( Chung et al . , 2014 ) , to control the combinations in recursive structure .

With these two gating mechanisms , our model can better model the complicated combinations of features and capture the long dependency interactions .

In our previous works , we have investigated several different topological structures ( tree and directed acyclic graph ) to recursively model the semantic composition from the bottom layer to the top layer , and applied them on Chinese word segmentation ( Chen et al . , 2015a ) and dependency parsing ( Chen et al . , 2015b ) tasks .

However , these structures are not suitable for modeling sentences .

i P(•|x i ; θ ) w 2 w 1 ( i ) ( i ) … … … … … … … … w 3 w 4 w 5 ( i ) ( i ) ( i ) 0 0 0

Figure 2 : Architecture of Gated Recursive Neural Network ( GRNN ) .

In this paper , we adopt the full binary tree as the topological structure to reduce the model complexity .

Experiments on the Stanford Sentiment Treebank dataset ( Socher et al . , 2013b ) and the TREC questions dataset ( Li and Roth , 2002 ) show the effectiveness of our approach .

In this paper , we propose a gated recursive neural network ( GRNN ) to recursively summarize the meaning of sentence .

GRNN uses full binary tree as the recursive topological structure instead of an external syntactic tree .

In addition , we introduce two kinds of gates to model the complicated combinations of features .

In future work , we would like to investigate the other gating mechanisms for better modeling the feature combinations .