Query Intent Recognition Based on Multi-Class Features

Qiu, Lirong; Chen, Yida; Jia, Heng-Yue; Zhang, Zhen

doi:10.1109/access.2018.2869585

Cited by 15 publications

(9 citation statements)

References 15 publications

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“…Adversarial training method for the multi-task and multi-lingual joint modelling [Mohasseb et al 2018] Grammar feature exploration Grammar-based framework with 3 main features [Xie et al 2018] Short text; Semantic feature expansion Semantic Tag-empowered combined features [Qiu et al 2018] Potential consciousness information mining A similarity calculation method based on LSTM and a traditional machine learning method based on multi-feature extraction OOD utterances Multi-task learning [Cohan et al 2019] Utilisation of naturally labelled data Multitask learning based on joint loss [Shridhar et al 2019] OOV issue; Small/lack of labelled training data Subword semantic hashing ] Learning of deep semantic information Hybrid CNN and bidirectional GRU neural network with pretrained embeddings (Char-CNN-BGRU) [Lin and Xu 2019] Emerging intents detection Maximise inter-class variance and minimise intra-class variance to get the discriminative feature [Ren and Xue 2020] Similar utterance with different intent Triples of samples used for training [Yilmaz and Toraman 2020] OOD utterances KL divergence vector for classification [Costello et al 2018] developed a novel multi-layer ensembling approach that ensembles both different model initialisation and different model architectures to determine how multi-layer ensembling improves performance on multilingual intent classification. They constructed a CNN with character-level embedding and a bidirectional CNN with attention mechanism.…”

Section: Papermentioning

confidence: 99%

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A survey of joint intent detection and slot-filling models in natural language understanding

Weld¹,

Huang²,

Long³

et al. 2021

Preprint

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Intent classification and slot filling are two critical tasks for natural language understanding. Traditionally the two tasks have been deemed to proceed independently. However, more recently, joint models for intent classification and slot filling have achieved state-of-the-art performance, and have proved that there exists a strong relationship between the two tasks. This article is a compilation of past work in natural language understanding, especially joint intent classification and slot filling. We observe three milestones in this research so far: Intent detection to identify the speaker's intention, slot filling to label each word token in the speech/text, and finally, joint intent classification and slot filling tasks. In this article, we describe trends, approaches, issues, data sets, evaluation metrics in intent classification and slot filling. We also discuss representative performance values, describe shared tasks, and provide pointers to future work, as given in prior works. To interpret the state-of-the-art trends, we provide multiple tables that describe and summarise past research along different dimensions, including the types of features, base approaches, and dataset domain used.

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Section: Papermentioning

confidence: 99%

“…Rather than temporal information [Qiu et al 2018] proposed the construction of multiple features from user metadata, regex extraction of named entities, and probabilistic context free grammar of composite entities.…”

Section: Contextual/temporal Information Modellingmentioning

confidence: 99%

A survey of joint intent detection and slot-filling models in natural language understanding

Weld¹,

Huang²,

Long³

et al. 2021

Preprint

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show abstract

“…Peningkatan efektivitas mesin pencari untuk menampilkan data yang relevan dapat dilakukan dengan menyediakan fungsi pencarian yang memahami maksud pengguna. Menurut Qiu et al (2018), penelitian tentang teknologi pencarian informasi berdasarkan maksud pengguna telah banyak dilakukan setelah taksonomi pencarian web disusun oleh Broder (2002). Salah satu arah penelitian tersebut adalah penerapan klasifikasi kueri pada mesin pencari.…”

Section: Pendahuluanunclassified

“…Pada penelitian Qiu et al (2018) telah dibangun metode klasifikasi kueri menggunakan LSTM similarity dan time sequence model untuk mengenali maksud dari kueri dalam pencarian data individu. Kemudian, penelitian Bortnikova et al (2019) telah membangun model neural network yang diintegrasikan dengan mesin pencari guna mengklasifikasikan kueri terhadap lima kategori.…”

Section: Pendahuluanunclassified

Penerapan Klasifikasi Kueri untuk Meningkatkan Efektivitas Mesin Pencari

Geraldy¹,

Maghfiroh²

2021

semnasoffstat

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Dalam menjalankan peran sebagai penyedia data, Badan Pusat Statistik (BPS) memberikan layanan akses data BPS bagi masyarakat. Salah satu layanan tersebut adalah fitur pencarian di website BPS. Namun, layanan pencarian yang diberikan belum memenuhi harapan konsumen. Untuk memenuhi harapan konsumen, salah satu upaya yang dapat dilakukan adalah meningkatkan efektivitas pencarian agar lebih relevan dengan maksud pengguna. Oleh karena itu, penelitian ini bertujuan untuk membangun fungsi klasifikasi kueri pada mesin pencari dan menguji apakah fungsi tersebut dapat meningkatkan efektivitas pencarian. Fungsi klasifikasi kueri dibangun menggunakan model machine learning. Kami membandingkan lima algoritma yaitu SVM, Random Forest, Gradient Boosting, KNN, dan Naive Bayes. Dari lima algoritma tersebut, model terbaik diperoleh pada algoritma SVM. Kemudian, fungsi tersebut diimplementasikan pada mesin pencari yang diukur efektivitasnya berdasarkan nilai precision dan recall. Hasilnya, fungsi klasifikasi kueri dapat mempersempit hasil pencarian pada kueri tertentu, sehingga meningkatkan nilai precision. Namun, fungsi klasifikasi kueri tidak memengaruhi nilai recall.

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“…Popular methods include recurrent neural network (RNN) [26], the joint model for ID and SF [27], and the attention-based model [28]. Most of these studies focus on simple query statements, but the performance improvement in task understanding for service robots is not significant [29]. The reason may be that common query task descriptions, such as ''What are the flights from Tacoma to San jose,'' differ from service language which usually contains verbs carrying core instructional information, such as ''carry'' in ''Could you please help me carry this book to the bedroom.''…”

Section: Related Workmentioning

confidence: 99%

A Novel Slot-Gated Model Combined With a Key Verb Context Feature for Task Request Understanding by Service Robots

Zhang

Jiang

et al. 2019

IEEE Access

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Spoken language understanding (SLU) is a fundamental to service robot handling of natural language task requests. There are two main basic problems in SLU, namely, intent determination (ID) and slot filling (SF). The slot-gated recurrent neural network joint model for the two tasks has been proven to be superior to the single model, and has achieved the most advanced performance. However, in the context of task requests for home service robots, there exists a phenomenon that the information about a current word is strongly dependent on key verbs in the sentence, and it is difficult to capture this relation well with current methods. In this paper, we extract the key instructional verb containing greater core task information based on dependency parsing, and construct a feature that combines the key verb with its contextual information to solve this problem. To further improve the performance of the slot-gated model, we consider the strong relations between intent and slot. By introducing intent attention vectors into the slot attention vectors through the global-level gate and element-level gate, a novel dual slot-gated mechanism is proposed to explicitly model the complex relations between the results of the ID tasks and SF prediction tasks and optimize the global prediction results. Our experimental results on the ATIS dataset and an extended home service task (SRTR) dataset based on FrameNet show that the proposed method outperforms the most advanced methods in both tasks. Especially, for SRTR, the results of SF, ID, and sentence-level semantic frame-filling are improved by 1.7%, 1.1%, and 1.7%, respectively.INDEX TERMS Human-robot interaction, service robots, slot-gated mechanism, spoken language understanding, verb context feature. ambiguity of verbs and incomplete natural language description scenarios [14]. In recent years, with the development of recurrent neural networks, some joint training models based on Long Short-Term Memory(LSTM) and its variants have become widely used in natural language understanding. These models are different from traditional verb matching and other context-free grammar models. They can learn the remote dependencies in sentences through the gating mechanism, and improve the overall performance through joint training of ID and SF. However, this kind of model only adds training loss for the joint training, which cannot model the complex correlation between the two tasks well. Moreover, in the field of service robots, people usually interact with robots in a command language. Unlike auxiliary query languages, many commands in this field contain key verbs that are directly related to behavioral intent. There is a strong information complement relation between many chunks in the command and such key verbs, while there is a weak dependence relation with other chunks. In the example of ''After five minutes, please wipe the table with a paper towel.'' shown in Figure 1 (a), the information of ''Time'', ''Tool'' and ''Target Object'' of ''wipe'' are complemented by ''After five minutes...

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Query Intent Recognition Based on Multi-Class Features

Cited by 15 publications

References 15 publications

A survey of joint intent detection and slot-filling models in natural language understanding

A survey of joint intent detection and slot-filling models in natural language understanding

Penerapan Klasifikasi Kueri untuk Meningkatkan Efektivitas Mesin Pencari

A Novel Slot-Gated Model Combined With a Key Verb Context Feature for Task Request Understanding by Service Robots

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