Joint Coreference Resolution and Character Linking for Multiparty Conversation (2021)

Abstract: Character linking, the task of linking mentioned people in conversations to the real world, is crucial for understanding the conversations. For the efficiency of communication, humans often choose to use pronouns (e.g., “she”) or normal entities (e.g., “that girl”) rather than named entities (e.g., “Rachel”) in the spoken language, which makes linking those mentions to real people a much more challenging than a regular entity linking task. To address this challenge, we propose to incorporate the richer context from the coreference relations among different mentions to help the linking. On the other hand, considering that finding coreference clusters itself is not a trivial task and could benefit from the global character information, we propose to jointly solve these two tasks. Specifically, we propose Cˆ2, the joint learning model of Coreference resolution and Character linking. The experimental results demonstrate that Cˆ2 can significantly outperform previous works on both tasks. Further analyses are conducted to analyze the contribution of all modules in the proposed model and the effect of all hyper-parameters.

## introduction understanding conversations has long been one of the ultimate goals of the natural language processing community, and a critical step towards that is grounding all mentioned people to the real world. if we can achieve that, we can leverage our knowledge about these people (e.g., things that happened to them before) to better understand the conversation. on the other hand, we can also aggregate the conversation information back to our understanding about these people, which can be used for understanding future conversations that involve the same people. to simulate the real conversations and investigate the possibility for models to ground mentioned people, the character linking task was proposed @xcite . specifically, it uses the transcripts of tv shows (i.e., friends) as the conversations and asks the models to ground all person mentions to characters. even though the character linking task can be viewed as a special case of the entity linking task, it is more challenging than the ordinary entity linking task for various reasons. first, the ordinary entity linking task often aims at linking named entities to external knowledge bases such as wikipedia, where rich information (e.g., definitions) are available. however, for the character linking task, we do not have the support of such rich knowledge base and all we have are the names of these characters and simple properties (e.g., gender) about these characters. second, the mentions in the ordinary entity linking are mostly concepts and entities, but not pronouns. however, as shown in figure 1 , 88% of the character mentions are pronouns (e.g., "he") or personal nouns (e.g., "that guy") while only 12% are named entities. considering that pronouns have relatively weak semantics by themselves, to effectively ground mentions to the correct characters, we need to fully utilize the context information of the whole conversation rather than just the local context they appear in. one potential solution is using the coreference relations among different mentions as the bridge to monica : there's nothing to tell! he 's just some guy i work with! joey : c'mon, you're going out with the guy ! there's gotta be something wrong with him ! ross: all right joey, be nice. so does he have a hump? a hump and a hairpiece? paul the wine guy connect the richer context. one example is shown in figure 2 . it is difficult to directly link the highlighted mentions to the character paul based on their local context because the local context of each mention can only provide a single piece of information about its referent, e.g., "person is a male" or "the person works with monica." given the coreference cluster, the mentions refer to the same person, and the pieces of information are put together to jointly determining the referent. as a result, it is easier for a model to do character linking with resolved coreference. similar observations are also made in @xcite . at the same time, we also noticed that coreference resolution, especially those involving pronouns, is also not trivial. as shown by the recent literature on the coreference resolution task @xcite , the task is still challenging for current models and the key challenge is how to utilize the global information about entities. and that is exactly what the character linking model can provide. for example, in figure 2 , it is difficult for a coreference model to correctly resolve the last mention he in the utterance given by ross based on its local context, because another major male character (joey) joins the conversation, which can distract and mislead the coreference model. however, if the model knows the mention he links to the character paul and paul works with monica, it is easier to resolve he to some guy that monica works with. motivated by these observations, we propose to jointly train the coreference resolution and character linking tasks and name the joint model as c 2 . c 2 adopts a transformer-based text encoder and includes a mention-level self-attention (mlsa) module that enables the model to do mention-level contextualization. meanwhile, a joint loss function is designed and utilized so that both tasks can be jointly optimized. the experimental results demonstrate that c 2 outperforms all previous work significantly on both tasks. specifically, compared with the previous work @xcite , c 2 improves the performance by 15% and 26% on the coreference resolution and character linking tasks foot_0 respectively comparing to the previous state-of-the-art model acnn @xcite . further hyper-parameter and ablation studies testify the effectiveness of different components of c 2 and the effect of all hyper-parameters. our code is available at https: //github.com/hkust-knowcomp/c2. ## problem formulations and notations we first introduce the coreference resolution and character linking tasks as well as used notations. given a conversation, which contains multiple utterances and n character mentions c 1 , c 2 , ..., c n , and a pre-defined character set z, which contains m characters z 1 , z 2 , ..., z m . the coreference resolution task is grouping all mentions to clusters such that all mentions in the same cluster refer to the same character. the character linking task is linking each mention to its corresponding character. ## model in this section, we introduce the proposed c 2 framework, which is illustrated in figure 3 . with the conversation and all mentions as input, we first encode them with a shared mention representation encoder module, which includes a pre-trained transformer text encoder and a mention-level selfattention (mlsa) module. after that, we make predictions for both tasks via two separate modules. in the end, a joint loss function is devised so that the model can be effectively trained on both tasks simultaneously. details are as follows. ## experiments in this section, we introduce the experimental details to demonstrate the effectiveness of c 2 . ## results and analysis in this section, we discuss the experimental results and present a detailed analysis. ## related works coreference resolution is the task of grouping mentions to clusters such that all the mentions in the same cluster refer to the same real-world entity @xcite @xcite . with the help of higher-order coreference resolution mechanism @xcite and strong pre-trained language models (e.g., span-bert @xcite ), the end-to-end based coreference resolution systems have been achieving impressive performance on the standard evaluation dataset @xcite . recently, motivated by the success of the transfer learning, @xcite propose to model the coreference resolution task as a question answering problem. through the careful fine-tuning on a high-quality qa dataset (i.e., squad-2.0 @xcite ), it achieves the state-of-the-art performance on the standard evaluation benchmark. however, as disclosed by @xcite , current systems are still not perfect. for example, they still cannot effectively handle pronouns, especially those in informal language usage scenarios like conversations. in this paper, we propose to leverage the out-of-context character information to help resolve the coreference relations with a joint learning model, which has been proven effective in the experiments. as a traditional nlp task, entity linking @xcite @xcite @xcite aims at linking mentions in context to entities in the real world (typically in the format of knowledge graph). typically, the mentions are named entities and the main challenge is the disambiguation. however, as a special case of the entity linking, the character linking task has its challenge that the majority of the mentions are pronouns. in the experiments, we have demonstrated that when the local context is not enough, the richer context information provided by the coreference clusters could be very helpful for linking mentions to the correct characters. in the nlp community, people have long been thinking that the coreference resolution task and entity linking should be able to help each other. for example, @xcite show how to use knowledge from named-entity linking to improve the coreference resolution, but do not consider doing it in a joint learning approach. after that, @xcite demonstrate that the coreference resolution and entity linking are complementary in terms of reducing the errors in both tasks. motivated by these observations, a joint model for coreference, typing, and linking is proposed @xcite to improve the performance on three tasks at the same time. compared with previous works, the main contributions of this paper are two-fold: (1) we tackle the challenging character linking problem; (2) we design a novel mention representation encoding method, which has been shown effective on both the coreference resolution and character linking tasks. ## conclusion in this paper, we propose to solve the coreference resolution and character linking tasks jointly. the experimental results show that the proposed model c 2 performs better than all previous models on both tasks. detailed analysis is also conducted to show the contribution of different modules and the effect of the hyper-parameter.