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Found 40 papers, 17 papers with code
GCPG: A General Framework for Controllable Paraphrase Generation
Under GCPG, we reconstruct commonly adopted lexical condition (i. e., Keywords) and syntactical conditions (i. e., Part-Of-Speech sequence, Constituent Tree, Masked Template and Sentential Exemplar) and study the combination of the two types.
Re-examining the Role of Schema Linking in Text-to-SQL
By providing a schema linking corpus based on the Spider text-to-SQL dataset, we systematically study the role of schema linking.
DiVa: An Iterative Framework to Harvest More Diverse and Valid Labels from User Comments for Music
Based on the observation that such missing information may already be presented in user comments, we propose to study the automated music labeling in an essential but under-explored setting, where the model is required to harvest more diverse and valid labels from the users' comments given limited gold labels.
Adaptive Vague Preference Policy Learning for Multi-round Conversational Recommendation
To address this issue, we introduce a novel scenario called Vague Preference Multi-round Conversational Recommendation (VPMCR), which considers users' vague and volatile preferences in CRS. VPMCR employs a soft estimation mechanism to assign a non-zero confidence score for all candidate items to be displayed, naturally avoiding the over-filtering problem.
Knowing-how & Knowing-that: A New Task for Machine Comprehension of User Manuals
The machine reading comprehension (MRC) of user manuals has huge potential in customer service.
Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration
Conversational systems based on Large Language Models (LLMs), such as ChatGPT, show exceptional proficiency in context understanding and response generation.
A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects
Proactive dialogue systems, related to a wide range of real-world conversational applications, equip the conversational agent with the capability of leading the conversation direction towards achieving pre-defined targets or fulfilling certain goals from the system side.
Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors
In this paper, we propose Fine-Grained Translation Error Detection (FG-TED) task, aiming at identifying both the position and the type of translation errors on given source-hypothesis sentence pairs.
FFHR: Fully and Flexible Hyperbolic Representation for Knowledge Graph Completion
Specifically, they suffer from two main limitations: 1) existing Graph Convolutional Network (GCN) methods in hyperbolic space rely on tangent space approximation, which would incur approximation error in representation learning, and 2) due to the lack of inner product operation definition in hyperbolic space, existing methods can only measure the plausibility of facts (links) with hyperbolic distance, which is difficult to capture complex data patterns.
Reconciliation of Pre-trained Models and Prototypical Neural Networks in Few-shot Named Entity Recognition
Incorporating large-scale pre-trained models with the prototypical neural networks is a de-facto paradigm in few-shot named entity recognition.
Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task
In this paper, we present our submission to the sentence-level MQM benchmark at Quality Estimation Shared Task, named UniTE (Unified Translation Evaluation).
Alibaba-Translate China's Submission for WMT 2022 Metrics Shared Task
In this report, we present our submission to the WMT 2022 Metrics Shared Task.
ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction
We study automatic Contract Clause Extraction (CCE) by modeling implicit relations in legal contracts.
PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance
To facilitate conversational question answering (CQA) over hybrid contexts in finance, we present a new dataset, named PACIFIC.
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
To facilitate model learning, we further collect rich features of users and items as well as users' behavior history.
Draft, Command, and Edit: Controllable Text Editing in E-Commerce
To address this challenge, we explore a new draft-command-edit manner in description generation, leading to the proposed new task-controllable text editing in E-commerce.
Towards Complex Document Understanding By Discrete Reasoning
Document Visual Question Answering (VQA) aims to understand visually-rich documents to answer questions in natural language, which is an emerging research topic for both Natural Language Processing and Computer Vision.
RDU: A Region-based Approach to Form-style Document Understanding
Key Information Extraction (KIE) is aimed at extracting structured information (e. g. key-value pairs) from form-style documents (e. g. invoices), which makes an important step towards intelligent document understanding.
Tailor: A Prompt-Based Approach to Attribute-Based Controlled Text Generation
We experimentally find that these prompts can be simply concatenated as a whole to multi-attribute CTG without any re-training, yet raises problems of fluency decrease and position sensitivity.
A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems
In this work, we propose a novel Unified MultI-goal conversational recommeNDer system, namely UniMIND.
Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy
To this end, we contribute to advance the study of the proactive dialogue policy to a more natural and challenging setting, i. e., interacting dynamically with users.
CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System
The basic idea is to first learn a causal user model on historical data to capture the overexposure effect of items on user satisfaction.
KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
The progress of recommender systems is hampered mainly by evaluation as it requires real-time interactions between humans and systems, which is too laborious and expensive.
A Dual Prompt Learning Framework for Few-Shot Dialogue State Tracking
In this paper, we focus on how to utilize the language understanding and generation ability of pre-trained language models for DST.
DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network
Knowledge graph completion (KGC) has become a focus of attention across deep learning community owing to its excellent contribution to numerous downstream tasks.
POS-Constrained Parallel Decoding for Non-autoregressive Generation
However, in this work, we experimentally reveal that this assumption does not always hold for the text generation tasks like text summarization and story ending generation.
TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance
In this work, we extract samples from real financial reports to build a new large-scale QA dataset containing both Tabular And Textual data, named TAT-QA, where numerical reasoning is usually required to infer the answer, such as addition, subtraction, multiplication, division, counting, comparison/sorting, and the compositions.
Ranked #1 on
Question Answering
on TAT-QA
Advances and Challenges in Conversational Recommender Systems: A Survey
In this paper, we provide a systematic review of the techniques used in current CRSs.
Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering
Open-domain Question Answering (OpenQA) is an important task in Natural Language Processing (NLP), which aims to answer a question in the form of natural language based on large-scale unstructured documents.
Machine Reading Comprehension
Open-Domain Question Answering
Generalized Relation Learning with Semantic Correlation Awareness for Link Prediction
The current methods for the link prediction taskhavetwonaturalproblems:1)the relation distributions in KGs are usually unbalanced, and 2) there are many unseen relations that occur in practical situations.
GMH: A General Multi-hop Reasoning Model for KG Completion
To the best of our knowledge, there lacks a general framework that approaches multi-hop reasoning in mixed long-short distance reasoning scenarios.
Interactive Path Reasoning on Graph for Conversational Recommendation
Traditional recommendation systems estimate user preference on items from past interaction history, thus suffering from the limitations of obtaining fine-grained and dynamic user preference.
Rethinking Dialogue State Tracking with Reasoning
Tracking dialogue states to better interpret user goals and feed downstream policy learning is a bottleneck in dialogue management.
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users
In this work, we consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively.
Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure
Research into the area of multiparty dialog has grown considerably over recent years.
Ranked #7 on
Discourse Parsing
on Molweni
Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems
Recommender systems are embracing conversational technologies to obtain user preferences dynamically, and to overcome inherent limitations of their static models.
Revisit Automatic Error Detection for Wrong and Missing Translation -- A Supervised Approach
While achieving great fluency, current machine translation (MT) techniques are bottle-necked by adequacy issues.
Recent Advances in Neural Question Generation
Emerging research in Neural Question Generation (NQG) has started to integrate a larger variety of inputs, and generating questions requiring higher levels of cognition.
Explicit State Tracking with Semi-Supervision for Neural Dialogue Generation
However, the \emph{expensive nature of state labeling} and the \emph{weak interpretability} make the dialogue state tracking a challenging problem for both task-oriented and non-task-oriented dialogue generation: For generating responses in task-oriented dialogues, state tracking is usually learned from manually annotated corpora, where the human annotation is expensive for training; for generating responses in non-task-oriented dialogues, most of existing work neglects the explicit state tracking due to the unlimited number of dialogue states.
Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures
Existing solutions to task-oriented dialogue systems follow pipeline designs which introduces architectural complexity and fragility.
