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Symbolic music understanding, which refers to the understanding of music from the symbolic data (e.g., MIDI format, but not audio), covers many music applications such as genre classification, emotion classification, and music pieces matching. While good music representations are beneficial for these applications, the lack of training data hinders representation learning. Inspired by the success of pre-training models in natural language processing, in this paper, we develop MusicBERT, a large-scale pre-trained model for music understanding. To this end, we construct a large-scale symbolic music corpus that contains more than 1 million music songs. Since symbolic music contains more structural (e.g., bar, position) and diverse information (e.g., tempo, instrument, and pitch), simply adopting the pre-training techniques from NLP to symbolic music only brings marginal gains. Therefore, we design several mechanisms, including OctupleMIDI encoding and bar-level masking strategy, to enhance pre-training with symbolic music data. Experiments demonstrate the advantages of MusicBERT on four music understanding tasks, including melody completion, accompaniment suggestion, genre classification, and style classification. Ablation studies also verify the effectiveness of our designs of OctupleMIDI encoding and bar-level masking strategy in MusicBERT.

IJCNLP-AACL 2021

MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

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The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

technical paper

People debate on a variety of topics on online platforms such as Reddit, or Facebook. Debates can be lengthy, with users exchanging a wealth of information and opinions. However, conversations do not always go smoothly, and users sometimes engage in unsound argumentation techniques to prove a claim. These techniques are called fallacies. Fallacies are persuasive arguments that provide insufficient or incorrect evidence to support the claim. In this paper, we study the most frequent fallacies on Reddit, and we present them using the pragma-dialectical theory of argumentation. We construct a new annotated dataset of fallacies, using user comments containing fallacy mentions as noisy labels, and cleaning the data via crowdsourcing. Finally, we study the task of classifying fallacies using neural models. We find that generally the models perform better in the presence of conversational context.We have released the data and the code at github.com/sahaisaumya/informal_fallacies.

Breaking Down the Invisible Wall of Informal Fallacies in Online Discussions

Inferring social relations from dialogues is vital for building emotionally intelligent robots to interpret human language better and act accordingly. We model the social network as an And-or Graph, named SocAoG, for the consistency of relations among a group and leveraging attributes as inference cues. Moreover, we formulate a sequential structure prediction task, and propose an $\alpha$-$\beta$-$\gamma$ strategy to incrementally parse SocAoG for the dynamic inference upon any incoming utterance: (i) an $\alpha$ process predicting attributes and relations conditioned on the semantics of dialogues, (ii) a $\beta$ process updating the social relations based on related attributes, and (iii) a $\gamma$ process updating individual's attributes based on interpersonal social relations. Empirical results on DialogRE and MovieGraph show that our model infers social relations more accurately than the state-of-the-art methods. Moreover, the ablation study shows the three processes complement each other, and the case study demonstrates the dynamic relational inference.

SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues

We present a data-driven, end-to-end approach to transaction-based dialog systems that performs at near-human levels in terms of verbal response quality and factual grounding accuracy. We show that two essential components of the system produce these results: a sufficiently large and diverse, in-domain labeled dataset, and a neural network-based, pre-trained model that generates both verbal responses and API call predictions. In terms of data, we introduce TicketTalk, a movie ticketing dialog dataset with 23,789 annotated conversations. The conversations range from completely open-ended and unrestricted to more structured, both in terms of their knowledge base, discourse features, and number of turns. In qualitative human evaluations, model-generated responses trained on just 10,000 TicketTalk dialogs were rated to “make sense” 86.5% of the time, almost the same as human responses in the same contexts. Our simple, API-focused annotation schema results in a much easier labeling task making it faster and more cost effective. It is also the key component for being able to predict API calls accurately. We handle factual grounding by incorporating API calls in the training data, allowing our model to learn which actions to take and when. Trained on the same 10,000-dialog set, the model’s API call predictions were rated to be correct 93.9% of the time in our evaluations, surpassing the ratings for the corresponding human labels. We show how API prediction and response generation scores improve as the dataset size incrementally increases from 5000 to 21,000 dialogs. Our analysis also clearly illustrates the benefits of pre-training. To facilitate future work on transaction-based dialog systems, we are publicly releasing the TicketTalk dataset at https://git.io/JL8an.

TicketTalk: Toward human-level performance with end-to-end, transaction-based dialog systems

In this paper, we explore the ability to model and infer personality types of opponents, predict their responses, and use this information to adapt a dialog agent's high-level strategy in negotiation tasks. Inspired by the idea of incorporating a theory of mind (ToM) into machines, we introduce a probabilistic formulation to encapsulate the opponent's personality type during both learning and inference. We test our approach on the CraigslistBargain dataset (He et al. 2018) and show that our method using ToM inference achieves a 20% higher dialog agreement rate compared to baselines on a mixed population of opponents. We also demonstrate that our model displays diverse negotiation behavior with different types of opponents.

Improving Dialog Systems for Negotiation with Personality Modeling

In this paper, we propose Inverse Adversarial Training (IAT) algorithm for training neural dialogue systems to avoid generic responses and model dialogue history better. In contrast to standard adversarial training algorithms, IAT encourages the model to be sensitive to the perturbation in the dialogue history and therefore learning from perturbations. By giving higher rewards for responses whose output probability reduces more significantly when dialogue history is perturbed, the model is encouraged to generate more diverse and consistent responses. By penalizing the model when generating the same response given perturbed dialogue history, the model is forced to better capture dialogue history and generate more informative responses. Experimental results on two benchmark datasets show that our approach can better model dialogue history and generate more diverse and consistent responses. In addition, we point out a problem of the widely used maximum mutual information (MMI) based methods for improving the diversity of dialogue response generation models and demonstrate it empirically.

Learning from Perturbations: Diverse and Informative Dialogue Generation with Inverse Adversarial Training

Chinese Spell Checking (CSC) aims to detect and correct erroneous characters for user-generated text in the Chinese language. 
Most of the Chinese spelling errors are misused semantically, phonetically or graphically similar characters. 
Previous attempts noticed this phenomenon and try to use the similarity for this task. However, these methods use either heuristics or handcrafted confusion sets to predict the correct character. In this paper, we propose a Chinese spell checker called ReaLiSe, by directly leveraging the multimodal information of the Chinese characters. 
The ReaLiSe model tackles the CSC task by (1) capturing the semantic, phonetic and graphic information of the input characters, and (2) selectively mixing the information in these modalities to predict the correct output. Experiments on the SIGHAN benchmarks show that the proposed model outperforms strong baselines by a large margin.

Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking

In this paper, we propose a novel self-supervised opinion summarization framework TransSum, which models opinion summaries as translations operating on the low-dimensional aspect and sentiment embedding spaces. Specifically, we propose two contrastive objectives to learn the crucial aspect and sentiment embeddings of reviews, by taking advantage of the intra- and inter-group invariances that have not been considered in previous studies.
Furthermore, these embeddings can be used to reduce opinion redundancy and construct highly relevant reviews-summary pairs to train a supervised multi-input opinion summarization model.
Experimental results on three different domains show that TransSum outperforms several strong baselines in generating informative, relevant and low-redundant summaries, unveiling the effectiveness of our approach.

TransSum: Translating Aspect and Sentiment Embeddings for Self-Supervised Opinion Summarization

Image-text matching has been a popular research topic which bridges vision and language through semantic understanding. Recent works mainly focus on exploring the interactions between images and sentences to improve the performance without considering inference efficiency. Specifically, for the large scale databases, it is unacceptable to perform such time-consuming mechanisms between a query (text/image) and each candidate datapoint (image/text) in the whole retrieval set during inference. To tackle this problem, we propose a novel hashing based efficient inference module called HEI, which can be plugged into the existing framework to speed up the inference step without reducing the retrieval performance. In details, HEI learns to map the original datapoints into short binary hash codes and coarsely preserve the heterologous matching relationship. Thus, in the inference phase, the proposed HEI module uses the hash codes to quickly select a few candidate datapoints from the retrieval set for a given query. Then, the image-text matching model fine ranks the candidate set to find the matching datapoint. Extensive experiments on two widely used benchmark MS-COCO and Flickr30k with four baseline methods demonstrate the efficiency and effectiveness of our proposed HEI module.

Hashing based Efficient Inference for Image-Text Matching

We investigate if, given a simple symbol masking strategy,
self-attention models are capable of learning nested structures and
generalise over their depth. We do so in the simplest setting
possible, namely languages consisting of nested parentheses of several
kinds. We use encoder-only models, which we train to predict randomly
masked symbols, in a BERT-like fashion. We find that the accuracy is
well above random baseline, with accuracy consistently above 50% both
when increasing nesting depth and distances between training and
testing. However, we find that the predictions made correspond to a
simple parenthesis counting strategy, rather than a push-down
automaton. This suggests that self-attention models are not suitable
for tasks which require generalisation to more complex instances of
recursive structures than those found in the training set.

Can the Transformer Learn Nested Recursion with Symbol Masking?

Given a video, video grounding aims to retrieve a temporal moment that semantically corresponds to a language query. In this work, we propose a Parallel Attention Network with Sequence matching (SeqPAN) to address the challenges in this task: multi-modal representation learning, and target moment boundary prediction. We design a self-guided parallel attention module to effectively capture self-modal contexts and cross-modal attentive information between video and text. Inspired by sequence labeling tasks in natural language processing, we split the ground truth moment into begin, inside, and end regions. We then propose a sequence matching strategy to guide start/end boundary predictions using region labels. Experimental results on three datasets show that SeqPAN is superior to state-of-the-art methods. Furthermore, the effectiveness of the self-guided parallel attention module and the sequence matching module is verified.

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MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training

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Breaking Down the Invisible Wall of Informal Fallacies in Online Discussions

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Adversarial Learning for Discourse Rhetorical Structure Parsing

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MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training

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Next from IJCNLP-AACL 2021

Breaking Down the Invisible Wall of Informal Fallacies in Online Discussions

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Adversarial Learning for Discourse Rhetorical Structure Parsing

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

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