ymoslem/acl-abstracts huggingface dataset:
DialCrowd: A toolkit for easy dialog system assessment (2018)

When creating a dialog system, developers need to test each version to ensure that it is performing correctly. Recently the trend has been to test on large datasets or to ask many users to try out a system. Crowdsourcing has solved the issue of finding users, but it presents new challenges such as how to use a crowdsourcing platform and what type of test is appropriate. DialCrowd has been designed to make system assessment easier and to ensure the quality of the result. This paper describes DialCrowd, what specific needs it fulfills and how it works. It then relates a test of DialCrowd by a group of dialog system developer.
Leveraging Multimodal Dialog Technology for the Design of Automated and Interactive Student Agents for Teacher Training (2018)

We present a paradigm for interactive teacher training that leverages multimodal dialog technology to puppeteer custom-designed embodied conversational agents (ECAs) in student roles. We used the open-source multimodal dialog system HALEF to implement a small-group classroom math discussion involving Venn diagrams where a human teacher candidate has to interact with two student ECAs whose actions are controlled by the dialog system. Such an automated paradigm has the potential to be extended and scaled to a wide range of interactive simulation scenarios in education, medicine, and business where group interaction training is essential.
An Empirical Study of Self-Disclosure in Spoken Dialogue Systems (2018)

Self-disclosure is a key social strategy employed in conversation to build relations and increase conversational depth. It has been heavily studied in psychology and linguistic literature, particularly for its ability to induce self-disclosure from the recipient, a phenomena known as reciprocity. However, we know little about how self-disclosure manifests in conversation with automated dialog systems, especially as any self-disclosure on the part of a dialog system is patently disingenuous. In this work, we run a large-scale quantitative analysis on the effect of self-disclosure by analyzing interactions between real-world users and a spoken dialog system in the context of social conversation. We find that indicators of reciprocity occur even in human-machine dialog, with far-reaching implications for chatbots in a variety of domains including education, negotiation and social dialog.
Role play-based question-answering by real users for building chatbots with consistent personalities (2018)

Having consistent personalities is important for chatbots if we want them to be believable. Typically, many question-answer pairs are prepared by hand for achieving consistent responses; however, the creation of such pairs is costly. In this study, our goal is to collect a large number of question-answer pairs for a particular character by using role play-based question-answering in which multiple users play the roles of certain characters and respond to questions by online users. Focusing on two famous characters, we conducted a large-scale experiment to collect question-answer pairs by using real users. We evaluated the effectiveness of role play-based question-answering and found that, by using our proposed method, the collected pairs lead to good-quality chatbots that exhibit consistent personalities.
Addressing Objects and Their Relations: The Conversational Entity Dialogue Model (2018)

Statistical spoken dialogue systems usually rely on a single- or multi-domain dialogue model that is restricted in its capabilities of modelling complex dialogue structures, e.g., relations. In this work, we propose a novel dialogue model that is centred around entities and is able to model relations as well as multiple entities of the same type. We demonstrate in a prototype implementation benefits of relation modelling on the dialogue level and show that a trained policy using these relations outperforms the multi-domain baseline. Furthermore, we show that by modelling the relations on the dialogue level, the system is capable of processing relations present in the user input and even learns to address them in the system response.
Conversational Image Editing: Incremental Intent Identification in a New Dialogue Task (2018)

We present {\textquotedblleft}conversational image editing{\textquotedblright}, a novel real-world application domain combining dialogue, visual information, and the use of computer vision. We discuss the importance of dialogue incrementality in this task, and build various models for incremental intent identification based on deep learning and traditional classification algorithms. We show how our model based on convolutional neural networks outperforms models based on random forests, long short term memory networks, and conditional random fields. By training embeddings based on image-related dialogue corpora, we outperform pre-trained out-of-the-box embeddings, for intention identification tasks. Our experiments also provide evidence that incremental intent processing may be more efficient for the user and could save time in accomplishing tasks.
Fine-Grained Discourse Structures in Continuation Semantics (2018)

In this work, we are interested in the computation of logical representations of discourse. We argue that all discourse connectives are anaphors obeying different sets of constraints and show how this view allows one to account for the semantically parenthetical use of attitude verbs and verbs of report (e.g., think, say) and for sequences of conjunctions (A CONJ{\_}1 B CONJ{\_}2 C). We implement this proposal in event semantics using de Groote (2006)`s dynamic framework.
Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks (2018)

In this paper we have proposed a linguistically informed recursive neural network architecture for automatic extraction of cause-effect relations from text. These relations can be expressed in arbitrarily complex ways. The architecture uses word level embeddings and other linguistic features to detect causal events and their effects mentioned within a sentence. The extracted events and their relations are used to build a causal-graph after clustering and appropriate generalization, which is then used for predictive purposes. We have evaluated the performance of the proposed extraction model with respect to two baseline systems,one a rule-based classifier, and the other a conditional random field (CRF) based supervised model. We have also compared our results with related work reported in the past by other authors on SEMEVAL data set, and found that the proposed bi-directional LSTM model enhanced with an additional linguistic layer performs better. We have also worked extensively on creating new annotated datasets from publicly available data, which we are willing to share with the community.
Toward zero-shot Entity Recognition in Task-oriented Conversational Agents (2018)

We present a domain portable zero-shot learning approach for entity recognition in task-oriented conversational agents, which does not assume any annotated sentences at training time. Rather, we derive a neural model of the entity names based only on available gazetteers, and then apply the model to recognize new entities in the context of user utterances. In order to evaluate our working hypothesis we focus on nominal entities that are largely used in e-commerce to name products. Through a set of experiments in two languages (English and Italian) and three different domains (furniture, food, clothing), we show that the neural gazetteer-based approach outperforms several competitive baselines, with minimal requirements of linguistic features.
Identifying Explicit Discourse Connectives in German (2018)

We are working on an end-to-end Shallow Discourse Parsing system for German and in this paper focus on the first subtask: the identification of explicit connectives. Starting with the feature set from an English system and a Random Forest classifier, we evaluate our approach on a (relatively small) German annotated corpus, the Potsdam Commentary Corpus. We introduce new features and experiment with including additional training data obtained through annotation projection and achieve an f-score of 83.89.
Feudal Dialogue Management with Jointly Learned Feature Extractors (2018)

Reinforcement learning (RL) is a promising dialogue policy optimisation approach, but traditional RL algorithms fail to scale to large domains. Recently, Feudal Dialogue Management (FDM), has shown to increase the scalability to large domains by decomposing the dialogue management decision into two steps, making use of the domain ontology to abstract the dialogue state in each step. In order to abstract the state space, however, previous work on FDM relies on handcrafted feature functions. In this work, we show that these feature functions can be learned jointly with the policy model while obtaining similar performance, even outperforming the handcrafted features in several environments and domains.
Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems (2018)

Cross-domain natural language generation (NLG) is still a difficult task within spoken dialogue modelling. Given a semantic representation provided by the dialogue manager, the language generator should generate sentences that convey desired information. Traditional template-based generators can produce sentences with all necessary information, but these sentences are not sufficiently diverse. With RNN-based models, the diversity of the generated sentences can be high, however, in the process some information is lost. In this work, we improve an RNN-based generator by considering latent information at the sentence level during generation using conditional variational auto-encoder architecture. We demonstrate that our model outperforms the original RNN-based generator, while yielding highly diverse sentences. In addition, our model performs better when the training data is limited.
Coherence Modeling Improves Implicit Discourse Relation Recognition (2018)

The research described in this paper examines how to learn linguistic knowledge associated with discourse relations from unlabeled corpora. We introduce an unsupervised learning method on text coherence that could produce numerical representations that improve implicit discourse relation recognition in a semi-supervised manner. We also empirically examine two variants of coherence modeling: order-oriented and topic-oriented negative sampling, showing that, of the two, topic-oriented negative sampling tends to be more effective.
Adversarial Learning of Task-Oriented Neural Dialog Models (2018)

In this work, we propose an adversarial learning method for reward estimation in reinforcement learning (RL) based task-oriented dialog models. Most of the current RL based task-oriented dialog systems require the access to a reward signal from either user feedback or user ratings. Such user ratings, however, may not always be consistent or available in practice. Furthermore, online dialog policy learning with RL typically requires a large number of queries to users, suffering from sample efficiency problem. To address these challenges, we propose an adversarial learning method to learn dialog rewards directly from dialog samples. Such rewards are further used to optimize the dialog policy with policy gradient based RL. In the evaluation in a restaurant search domain, we show that the proposed adversarial dialog learning method achieves advanced dialog success rate comparing to strong baseline methods. We further discuss the covariate shift problem in online adversarial dialog learning and show how we can address that with partial access to user feedback.
Constructing a Lexicon of English Discourse Connectives (2018)

We present a new lexicon of English discourse connectives called DiMLex-Eng, built by merging information from two annotated corpora and an additional list of relation signals from the literature. The format follows the German connective lexicon DiMLex, which provides a cross-linguistically applicable XML schema. DiMLex-Eng contains 149 English connectives, and gives information on syntactic categories, discourse semantics and non-connective uses (if any). We report on the development steps and discuss design decisions encountered in the lexicon expansion phase. The resource is freely available for use in studies of discourse structure and computational applications.
Maximizing SLU Performance with Minimal Training Data Using Hybrid RNN Plus Rule-based Approach (2018)

Spoken language understanding (SLU) by using recurrent neural networks (RNN) achieves good performances for large training data sets, but collecting large training datasets is a challenge, especially for new voice applications. Therefore, the purpose of this study is to maximize SLU performances, especially for small training data sets. To this aim, we propose a novel CRF-based dialog act selector which chooses suitable dialog acts from outputs of RNN SLU and rule-based SLU. We evaluate the selector by using DSTC2 corpus when RNN SLU is trained by less than 1,000 training sentences. The evaluation demonstrates the selector achieves Micro F1 better than both RNN and rule-based SLUs. In addition, it shows the selector achieves better Macro F1 than RNN SLU and the same Macro F1 as rule-based SLU. Thus, we confirmed our method offers advantages in SLU performances for small training data sets.
An Analysis of the Effect of Emotional Speech Synthesis on Non-Task-Oriented Dialogue System (2018)

This paper explores the effect of emotional speech synthesis on a spoken dialogue system when the dialogue is non-task-oriented. Although the use of emotional speech responses have been shown to be effective in a limited domain, e.g., scenario-based and counseling dialogue, the effect is still not clear in the non-task-oriented dialogue such as voice chatting. For this purpose, we constructed a simple dialogue system with example- and rule-based dialogue management. In the system, two types of emotion labeling with emotion estimation are adopted, i.e., system-driven and user-cooperative emotion labeling. We conducted a dialogue experiment where subjects evaluate the subjective quality of the system and the dialogue from the multiple aspects such as richness of the dialogue and impression of the agent. We then analyze and discuss the results and show the advantage of using appropriate emotions for the expressive speech responses in the non-task-oriented system.
Multi-task Learning for Joint Language Understanding and Dialogue State Tracking (2018)

This paper presents a novel approach for multi-task learning of language understanding (LU) and dialogue state tracking (DST) in task-oriented dialogue systems. Multi-task training enables the sharing of the neural network layers responsible for encoding the user utterance for both LU and DST and improves performance while reducing the number of network parameters. In our proposed framework, DST operates on a set of candidate values for each slot that has been mentioned so far. These candidate sets are generated using LU slot annotations for the current user utterance, dialogue acts corresponding to the preceding system utterance and the dialogue state estimated for the previous turn, enabling DST to handle slots with a large or unbounded set of possible values and deal with slot values not seen during training. Furthermore, to bridge the gap between training and inference, we investigate the use of scheduled sampling on LU output for the current user utterance as well as the DST output for the preceding turn.
Weighting Model Based on Group Dynamics to Measure Convergence in Multi-party Dialogue (2018)

This paper proposes a new weighting method for extending a dyad-level measure of convergence to multi-party dialogues by considering group dynamics instead of simply averaging. Experiments indicate the usefulness of the proposed weighted measure and also show that in general a proper weighting of the dyad-level measures performs better than non-weighted averaging in multiple tasks.
Concept Transfer Learning for Adaptive Language Understanding (2018)

Concept definition is important in language understanding (LU) adaptation since literal definition difference can easily lead to data sparsity even if different data sets are actually semantically correlated. To address this issue, in this paper, a novel concept transfer learning approach is proposed. Here, substructures within literal concept definition are investigated to reveal the relationship between concepts. A hierarchical semantic representation for concepts is proposed, where a semantic slot is represented as a composition of \textit{atomic concepts}. Based on this new hierarchical representation, transfer learning approaches are developed for adaptive LU. The approaches are applied to two tasks: value set mismatch and domain adaptation, and evaluated on two LU benchmarks: ATIS and DSTC 2{\&}3. Thorough empirical studies validate both the efficiency and effectiveness of the proposed method. In particular, we achieve state-of-the-art performance (F₁-score 96.08{\%}) on ATIS by only using lexicon features.
Cogent: A Generic Dialogue System Shell Based on a Collaborative Problem Solving Model (2018)

The bulk of current research in dialogue systems is focused on fairly simple task models, primarily state-based. Progress on developing dialogue systems for more complex tasks has been limited by the lack generic toolkits to build from. In this paper we report on our development from the ground up of a new dialogue model based on collaborative problem solving. We implemented the model in a dialogue system shell (Cogent) that al-lows developers to plug in problem-solving agents to create dialogue systems in new domains. The Cogent shell has now been used by several independent teams of researchers to develop dialogue systems in different domains, with varied lexicons and interaction style, each with their own problem-solving back-end. We believe this to be the first practical demonstration of the feasibility of a CPS-based dialogue system shell.
Identifying Domain Independent Update Intents in Task Based Dialogs (2018)

One important problem in task-based conversations is that of effectively updating the belief estimates of user-mentioned slot-value pairs. Given a user utterance, the intent of a slot-value pair is captured using dialog acts (DA) expressed in that utterance. However, in certain cases, DA`s fail to capture the actual update intent of the user. In this paper, we describe such cases and propose a new type of semantic class for user intents. This new type, Update Intents (UI), is directly related to the type of update a user intends to perform for a slot-value pair. We define five types of UI`s, which are independent of the domain of the conversation. We build a multi-class classification model using LSTM`s to identify the type of UI in user utterances in the Restaurant and Shopping domains. Experimental results show that our models achieve strong classification performance in terms of F-1 score.
Neural Character-based Composition Models for Abuse Detection (2018)

The advent of social media in recent years has fed into some highly undesirable phenomena such as proliferation of offensive language, hate speech, sexist remarks, etc. on the Internet. In light of this, there have been several efforts to automate the detection and moderation of such abusive content. However, deliberate obfuscation of words by users to evade detection poses a serious challenge to the effectiveness of these efforts. The current state of the art approaches to abusive language detection, based on recurrent neural networks, do not explicitly address this problem and resort to a generic OOV (out of vocabulary) embedding for unseen words. However, in using a single embedding for all unseen words we lose the ability to distinguish between obfuscated and non-obfuscated or rare words. In this paper, we address this problem by designing a model that can compose embeddings for unseen words. We experimentally demonstrate that our approach significantly advances the current state of the art in abuse detection on datasets from two different domains, namely Twitter and Wikipedia talk page.
Hate Speech Dataset from a White Supremacy Forum (2018)

Hate speech is commonly defined as any communication that disparages a target group of people based on some characteristic such as race, colour, ethnicity, gender, sexual orientation, nationality, religion, or other characteristic. Due to the massive rise of user-generated web content on social media, the amount of hate speech is also steadily increasing. Over the past years, interest in online hate speech detection and, particularly, the automation of this task has continuously grown, along with the societal impact of the phenomenon. This paper describes a hate speech dataset composed of thousands of sentences manually labelled as containing hate speech or not. The sentences have been extracted from Stormfront, a white supremacist forum. A custom annotation tool has been developed to carry out the manual labelling task which, among other things, allows the annotators to choose whether to read the context of a sentence before labelling it. The paper also provides a thoughtful qualitative and quantitative study of the resulting dataset and several baseline experiments with different classification models. The dataset is publicly available.
A Review of Standard Text Classification Practices for Multi-label Toxicity Identification of Online Content (2018)

Language toxicity identification presents a gray area in the ethical debate surrounding freedom of speech and censorship. Today`s social media landscape is littered with unfiltered content that can be anywhere from slightly abusive to hate inducing. In response, we focused on training a multi-label classifier to detect both the type and level of toxicity in online content. This content is typically colloquial and conversational in style. Its classification therefore requires huge amounts of annotated data due to its variability and inconsistency. We compare standard methods of text classification in this task. A conventional one-vs-rest SVM classifier with character and word level frequency-based representation of text reaches 0.9763 ROC AUC score. We demonstrated that leveraging more advanced technologies such as word embeddings, recurrent neural networks, attention mechanism, stacking of classifiers and semi-supervised training can improve the ROC AUC score of classification to 0.9862. We suggest that in order to choose the right model one has to consider the accuracy of models as well as inference complexity based on the application.
Predictive Embeddings for Hate Speech Detection on Twitter (2018)

We present a neural-network based approach to classifying online hate speech in general, as well as racist and sexist speech in particular. Using pre-trained word embeddings and max/mean pooling from simple, fully-connected transformations of these embeddings, we are able to predict the occurrence of hate speech on three commonly used publicly available datasets. Our models match or outperform state of the art F1 performance on all three datasets using significantly fewer parameters and minimal feature preprocessing compared to previous methods.
Challenges for Toxic Comment Classification: An In-Depth Error Analysis (2018)

Toxic comment classification has become an active research field with many recently proposed approaches. However, while these approaches address some of the task`s challenges others still remain unsolved and directions for further research are needed. To this end, we compare different deep learning and shallow approaches on a new, large comment dataset and propose an ensemble that outperforms all individual models. Further, we validate our findings on a second dataset. The results of the ensemble enable us to perform an extensive error analysis, which reveals open challenges for state-of-the-art methods and directions towards pending future research. These challenges include missing paradigmatic context and inconsistent dataset labels.