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Found 59 papers, 28 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.
PAGED: A Benchmark for Procedural Graphs Extraction from Documents
To this end, we propose a new benchmark PAGED, equipped with a large high-quality dataset and standard evaluations.
CARE: A Clue-guided Assistant for CSRs to Read User Manuals
It is time-saving to build a reading assistant for customer service representations (CSRs) when reading user manuals, especially information-rich ones.
Legend: Leveraging Representation Engineering to Annotate Safety Margin for Preference Datasets
By leveraging this safety direction, Legend can then leverage the semantic distances of paired responses along this direction to annotate margins automatically.
Dishonesty in Helpful and Harmless Alignment
We find that this also induces dishonesty in helpful and harmless alignment where LLMs tell lies in generating harmless responses.
Backpropagation-Free Multi-modal On-Device Model Adaptation via Cloud-Device Collaboration
As these systems grapple with shifting data distributions between the cloud and devices, the traditional approach of fine-tuning-based adaptation (FTA) exists the following issues: the costly and time-consuming data annotation required by FTA and the looming risk of model overfitting.
CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models
To this end, we introduce CLAMBER, a benchmark for evaluating LLMs using a well-organized taxonomy.
Selective Annotation via Data Allocation: These Data Should Be Triaged to Experts for Annotation Rather Than the Model
As such, informative or hard data is assigned to the expert for annotation, while easy data is handled by the model.
ARAIDA: Analogical Reasoning-Augmented Interactive Data Annotation
To address this issue, interactive data annotation utilizes an annotation model to provide suggestions for humans to approve or correct.
STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agents
However, they still struggle to deliver promising performance on unseen domains, struggling to achieve effective domain transferability.
Co-Matching: Towards Human-Machine Collaborative Legal Case Matching
However, successful legal case matching requires the tacit knowledge of legal practitioners, which is difficult to verbalize and encode into machines.
Towards Analyzing and Understanding the Limitations of DPO: A Theoretical Perspective
Direct Preference Optimization (DPO), which derives reward signals directly from pairwise preference data, has shown its effectiveness on aligning Large Language Models (LLMs) with human preferences.
Concept -- An Evaluation Protocol on Conversational Recommender Systems with System-centric and User-centric Factors
The conversational recommendation system (CRS) has been criticized regarding its user experience in real-world scenarios, despite recent significant progress achieved in academia.
Strength Lies in Differences! Towards Effective Non-collaborative Dialogues via Tailored Strategy Planning
We investigate non-collaborative dialogue agents, which are expected to engage in strategic conversations with diverse users, for securing a mutual agreement that leans favorably towards the system's objectives.
Cross-Space Adaptive Filter: Integrating Graph Topology and Node Attributes for Alleviating the Over-smoothing Problem
To this end, various methods have been proposed to create an adaptive filter by incorporating an extra filter (e. g., a high-pass filter) extracted from the graph topology.
DREditor: An Time-efficient Approach for Building a Domain-specific Dense Retrieval Model
Motivated by this, we develop a time-efficient approach called DREditor to edit the matching rule of an off-the-shelf dense retrieval model to suit a specific domain.
See the Unseen: Better Context-Consistent Knowledge-Editing by Noises
In this paper, we empirically find that the effects of different contexts upon LLMs in recalling the same knowledge follow a Gaussian-like distribution.
Towards Equipping Transformer with the Ability of Systematic Compositionality
One of the key factors in language productivity and human cognition is the ability of systematic compositionality, which refers to understanding composed unseen examples of seen primitives.
Towards Goal-oriented Intelligent Tutoring Systems in Online Education
In this work, we investigate a new task, named Goal-oriented Intelligent Tutoring Systems (GITS), which aims to enable the student's mastery of a designated concept by strategically planning a customized sequence of exercises and assessment.
OccuQuest: Mitigating Occupational Bias for Inclusive Large Language Models
Furthermore, we assemble three test sets for comprehensive evaluation, an occu-test set covering 25 occupational categories, an estate set focusing on real estate, and an occu-quora set containing real-world questions from Quora.
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.
Vague Preference Policy Learning for Conversational Recommendation
We introduce the Vague Preference Multi-round Conversational Recommendation (VPMCR) scenario, employing a soft estimation mechanism to accommodate users' vague and dynamic preferences while mitigating over-filtering.
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.
