DMIN : A Discourse-specific Multi-granularity Integration Network for Conversational Aspect-based Sentiment Quadruple Analysis (2024)

Abstract: Conversational Aspect-based Sentiment Quadruple Analysis (DiaASQ) aims to extract fine-grained sentiment quadruples from dialogues. Previous research has primarily concentrated on enhancing token-level interactions, still lacking in sufficient modeling of the discourse structure information in dialogue. Firstly, it does not incorporate interactions among different utterances in the encoding stage, resulting in a limited token-level context understanding for subsequent modules. Secondly, it ignores the critical fact that discourse information is naturally organized at the utterance level and learning it solely at the token level is incomplete. In this work, we strengthen the token-level encoder by utilizing a discourse structure called “thread” and graph convolutional networks to enhance the token interaction among different utterances. Moreover, we propose an utterance-level encoder to learn the structured speaker and reply information, providing a macro understanding of dialogue discourse. Furthermore, we introduce a novel Multi-granularities Integrator to integrate token-level and utterance-level representations, resulting in a comprehensive and cohesive dialogue contextual understanding. Experiments on two datasets demonstrate that our model achieves state-of-the-art performance. Our codes are publicly available at https://github.com/SIGSDSscau/DMIN.

## introduction conversational aspect-based sentiment quadruple analysis (diaasq) @xcite is a new compound subtask of aspect-based sentiment analysis (absa). as shown in figure 1 , diaasq aims to extract all (t, a, o, s) sentiment quadruples present in a conversation, where the target t (the subject of discussion), aspect a (specific attribute of target) and opinion o (attitude or evaluation towards † equal contribution. * corresponding author. aspect) represent substrings of dialogue, and sentiment s represents one of the categories of positive, negative, or neutral. given an example sentence, "iphone loses power very fast.", the corresponding elements are "iphone", "loses power", "very fast", and "negative", respectively. each quadruple can be considered as a fine-grained opinion that conveys sentiment towards a specific aspect of the target. as shown in figure 1 , the dialogue has a structural discourse, including multiple utterances and corresponding speakers, and except for the root utterance, each speaker's utterance has a reply object. compared to aspect-based sentiment quad prediction (asqp) @xcite , another subtask of absa that extracts quadruples from plain text, diaasq faces two additional challenges. on the one hand, the relatively lengthy nature of the dialogue makes it more challenging to establish long-term dependency relationships be-tween targets, aspects, and opinions. on the other hand, the dialogue encompasses crucial reply relationships and structured discourse information, making it a challenging task to model the discourse information of the dialogue accurately. specifically, the elements of the quadruples may come from different utterances. taking figure 1 as an example, the opinion term "not bad" in speaker-c's utterance is related to the target term "iphone 14" in speaker-b's utterance and the aspect term "power drops" in speaker-a's utterance. compared with intrautterance quadruples, such cross-utterance quadruples require a more comprehensive understanding of the interaction of the utterances and speakers. in diaasq, @xcite designed a new labeling scheme of grid tagging @xcite and proposed the mvqpn network, which models the discourse structure of conversations at the token level through three kinds of mask multi-head attention mechanisms @xcite . however, the model didn't capture the interaction of utterances in the encoding stage. @xcite tried to solve this issue by encoding the whole dialogue in the pretrain language models (plms) layer. @xcite changed the parallel attentions to continuous, deepening the network structure. though achieving a promising performance, the model's context modeling for structured conversation is incomplete and inadequate: (1) it does not consider the influence of other utterances when encoding each sentence, resulting in an incomplete contextual understanding for subsequent modules; in this work, we propose the discourse-specific multi-granularity integration network, dmin, to provide a more complete contextual understanding for structured conversation, enhancing the extraction of intra-utterance quads and cross-utterance quads. for the first issue mentioned above, we employ the concrete knowledge encoder (ckencoder) to capture syntactic and semantic information at the token level, which leverages the discourse structure called "thread" to enhance the interaction between different utterances. as shown in the top-right corner of figure 1 , a dialogue can be structured as a tree based on the reply relationships, and the so-called "thread" refers to the subtree derived from the root node of the conversation tree. for the second issue, we learn the speaker and reply relationships at the utterance level through a global discourse encoder (gdencoder). the gdencoder offers a more natural and comprehensive structural discourse information from a macro perspective. furthermore, as the two modules' information granularity and representation dimensions are different, direct fusion is not feasible. therefore, we propose a novel multi-granularity integrator to effectively combine the token-level and utterance-level information. in essence, diaasq requires the extraction of substrings from the text, necessitating that the model's minimum granularity must be at the token level. however, the natural organization of dialogue discourse structure is based on the utterance level, generating representations at the utterance level. our model provides these two kinds of complementary information and fuses them effectively, yielding a more comprehensive and cohesive contextual understanding. our contributions can be summarized as follows: ## methodology the overall architecture of our proposed dmin is shown in figure 2 . in diaasq, each dialog is represented as a training sample denoted as @xmath0, ..., u n } with the corresponding relying record @xmath1, ..., l n } and speakers @xmath2, ..., s n }, where relying record l i denotes the i-th utterance reply to the l i -th one. each utterance u @xmath3, ..., w m i } where m i is the length of u i . following the labeling scheme of grid tagging proposed by @xcite , we decompose the original diaasq task into three joint jobs, and the model aims to predict the entity boundary labels y @xmath4, asp, opi, other}, the entity pair labels y @xmath5, t2t, other} and the polarity labels y @xmath6, neg, neu, other}, where tgt, asp, and opi denote the token-level relations between the head and tail of a target, aspect, and opinion term, respectively (e.g., the label tgt between head token "iphone" and tail token "14" denotes a target term "iphone 14"). the labels h2h (head-to-head) and t2t (tail-to-tail) are used to align the head and tail tokens between a pair of entities in two types (e.g., the target's head token "iphone" and aspect's head token "battery" is connected by h2h, while the tail token "14" and "life" is connected with t2t). ## conclusion this paper addresses previous work's limitations and proposes a fresh viewpoint for diaasq to better model the discourse structure information in dialogue. specifically, we enhance the utterance interactions at the token-level granularity on the thread scale and then capture global discourse information at the utterance-level granularity on the speaker utterance 0 oneplus 1, 3, 7pro users, see how bad oneplus 9, 9pro, 9r, 9rt are this year. what? don't let me say 1 9pro and kazakh soviet co-branded is good for taking pictures [doge] 0 the mode updated later is a bit interesting. in the early stage, i feel that it is not as good as x3pro [allow sad] 1 harzu is good [like] 2 pro is ok, good workmanship 0 aesthetics are not good, and taking pictures with joint names is useless. if the workmanship is not good, it is not a oneplus. how can the workmanship be bad with the green factory production line. 3 we both use the same phone. id type gold label [t, a, o, p] mvqpn's pred ours dmin's pred 1 cross ['9pro', 'harzu', 'good', 'pos'] ✖ ✔ 2 cross ['9pro', 'mode', 'a bit interesting', 'pos'] ✖ ✔ 3 cross ['pro', 'aesthetics', 'not good', 'neg'] ✔ ✔ 4 cross ['pro', 'taking pictures', 'joint names is useless', 'pos'] ✖ ['pro', 'taking pictures', 'useless', 'pos'] 5 intra ['pro', 'workmanship', 'good', 'pos'] ['pro', 'workmanship', 'not good', 'neg'] ✖ 6 intra ['green factory', 'workmanship', 'how can the workmanship be bad', 'pos'] ✖ ✖ 7 intra ['9pro', 'taking pictures', 'good', 'pos'] ✔ ✔ 8 intra ['oneplus', 'workmanship', 'not good', 'neg'] ✖ ['oneplus', 'workmanship', 'bad', 'neu'] 9 error / / ['9pro', 'mode', 'not as good as', 'neg'] dialogue scale, which is more efficient and macro. furthermore, we introduce a novel integrator to tackle the challenge of integrating data across diverse granularities, yielding a comprehensive and cohesive contextual understanding. the experimental results demonstrate the effectiveness of our proposed dmin. zheng zhang, zili zhou, and yanna wang. 2022. ssegcn: syntactic and semantic enhanced graph convolutional network for aspect-based sentiment analysis. in proceedings of the 2022 conference of the north american chapter of the association for computational linguistics: human language technologies, pages 4916-4925, seattle, united states. association for computational linguistics.