The following relates to the customer service arts, call center operational arts, and related arts.
Call centers are common tools by which companies, governmental entities, and the like provide customer support, client support, or other assistance. Telephonic call centers are currently most common, but call centers employing other communication media are increasingly employed, such as Internet-driven text-based online chat. Besides problem diagnosis/resolution, call centers are also used for outreach—for example, telephonic product marketing and political campaigning provides tools for reaching customers and informing potential voters, respectively. In these latter applications, the call center agent initiates the call.
The staffing of call centers can be problematic. In problem diagnosis/resolution tasks, the call center is often expected to provide 24/7 response at any time of day and any day of the week, including weekends and holidays. On the other hand, the actual call load can vary widely depending upon time of day, day of week, or season or time of year. Depending upon the geographical area serviced by the call center, the call load may include multiple time zones, and service in multiple languages may be provided. In outreach tasks, call center activity may need to ramp up quickly, for example to encourage voting shortly before the day of an election.
Further, call center agents must have a multiplicity of skills, including both language/communication skills to effectively communicate and the technical skill to provide the required problem diagnosis and resolution. In the case of outreach tasks, call center agents should be knowledgeable about a product being marketed, or about a political candidate's views on issues being debated in a political campaign.
For some call centers, it may not be cost-effective, or even possible, to provide enough human call agents with the requisite communications and technical skills to fully staff the call center. A known way to address this problem is to provide a computerized call center dialog manager. Such a computerized dialog manager provides guidance for the substantive content of the call dialog. This can be open-loop, e.g. providing a dialog script that the call center agent is expected to follow. More sophisticated call center dialog managers employ feedback control by providing suggested questions for the call center agent to ask based on the dialog history.
In terms of substantive content, a computerized call center dialog manager can be effective, especially when feedback control is employed, since calls commonly follow a limited number of substantive paths. For a given product, there are a limited number of common failure modes, and each failure mode can be diagnosed and resolved using a common set of questions asked in a logical sequence. Similarly, the range of questions that a telephone marketer or political campaign outreach agent is likely to field is relatively small and can be determined, for example, by statistically analyzing a representative number of past calls handled by a skilled human call center agent.
On the other hand, the communication skills aspect of call center automation may be more challenging than the substantive content aspect. Communication skills require adept handling of natural language (spoken in the case of a telephone call, or written in the case of an online chat call), which effectively emulate the natural nuances achieved by human speakers who are fluent in the employed natural language. For example, the call center dialog manager may suggest asking the customer whether the television is plugged in (for example, based on past dialog substance indicating the television is completely nonresponsive)—but there are many ways to actually ask this question. One way: “Did you remember to plug the television into the electrical outlet?” may be interpreted by the customer as insulting, whereas another way “By the way, the television is plugged in, right?” may be interpreted as an amusing joke which nonetheless executes the desired question. Effective natural language communication also exhibits a certain spontaneity or dynamic variability that is difficult to quantify—but a human listener readily perceives, and is often annoyed by, the failure of a speaker to employ appropriate dynamic variability during a spoken or textual dialog.
One way to deal with the communication skills aspect is to employ human call center agents who are fluent in the natural language of the call, in conjunction with a computerized call center dialog manager. In this approach, the human call center agent performs the “translation” of questions suggested by the dialog manager into appropriate natural language utterances. This approach advantageously facilitates employment of call center agents who may lack the requisite technical skill (since this is provided by the computerized dialog manager), but cannot enable full automation. It still may be difficult to hire enough human call center agents with the requisite communication skills even after relaxing the technical skill requirement.
Improved efficiency can be obtained if the call center automation also provides the actual utterances to be asked, that is, the natural language “translation” of the substantive content provided by the dialog manager. In this way, completely automated call center operations can be achieved (except perhaps for the occasional complex or non-standard call, which may be transferred to a human agent). Alternatively, human call center agents may still be employed to speak the suggested utterances, with the automatically suggested utterances providing assistance to human call center agents who may not be fluent in the natural language used in a call.
In some illustrative embodiments disclosed herein, a call center device operates in conjunction with a telephonic or online chat communication station. The call center device comprises a dialog manager and an utterance generation component. The dialog manager is configured to determine a recommended agent dialog act based on past dialog between a call center agent and a second party which is received from the telephonic or online chat communication station. The utterance generation component is configured to generate at least one recommended agent utterance for implementing the recommended dialog act by operations including: ranking a set of word lattices each represented as a weighted finite state automaton (WFSA) by conditional probabilities p(τ|DA type) where τ is a word lattice and DA type is a dialog act type of the recommended dialog act; choosing at least one word lattice from the ranking; and instantiating the chosen at least one word lattice to generate the at least one recommended agent utterance. The dialog manager and utterance generation component may comprise at least one computer programmed to determine the recommended dialog act and to generate the at least one recommended agent utterance.
The call center device of the immediately preceding paragraph may further comprise a training device comprising at least one computer configured to construct the set of word lattices from training dialogs between call center agents and second parties and to assign to the word lattices τ the conditional probabilities p(τ|DA type) over dialog act types DA type. The set of word lattices may be constructed by operations including: extracting features to represent agent utterances of the training dialogs; clustering the agent utterances of the training dialogs represented by the extracted features to form agent utterance clusters; aligning the agent utterances of each cluster; and generating a word lattice represented as a WFSA encoding the aligned agent utterances of each cluster. Agent utterance features may, for example, include context features extracted from second party utterances of the training dialogs, and/or grammatical dependency link features characterizing grammatical dependencies between words within agent utterances. The generating of a word lattice may include determining weights of sub-sequences of the word lattice based on numbers of agent utterances merged to form the sub-sequences.
In the call center device of the two immediately preceding paragraphs, the training device may classify each path variation in the word lattices as a slot, a paraphrase, or neither, and during the instantiating of the chosen at least one word lattice. The utterance generation component is configured to substitute data provided by the dialog manager for any slot path variation and to choose one path of any paraphrase path variation based on weights of the paths of the paraphrase path variation.
In some embodiments disclosed herein, a non-transitory storage medium stores instructions that are readable and executable by one or more electronic data processing devices to perform a method for generating recommended agent utterances for implementing dialog acts recommended by a dialog manager of a call center. The method comprises constructing a set of word lattices each represented as a WFSA from training dialogs between call center agents and second parties, and assigning to the word lattices conditional probabilities over dialog act type. For each recommended dialog act received from the dialog manager, the set of word lattices are ranked by the conditional probabilities for the dialog act type of the recommended dialog act, at least one word lattice is chosen from the ranking, and the chosen at least one word lattice is instantiated to generate at least one recommended agent utterance for implementing the recommended dialog act.
A typical natural language generation (NLG) pipeline includes three steps: content planning; sentence planning; and surface realization. However, in a dialog scenario in which a automated call center agent interacts with a human (e.g. customer, client, potential voter, et cetera), a dialog manager (DM) takes the responsibility of planning the content to be generated given the state of the dialog. The DM communicates this information to the NLG component as content suggestions, referred to herein as dialog acts (DA). In this approach, a DA provides the substantive content to be conveyed by an utterance, including: (1) a communicative function (encoding the type of communication) also called the dialog act type (DA type) herein; and (2) content to be conveyed which is suitably obtained from a knowledge base (KB). Thus, in the call center dialog context, the NLG component is only responsible for handling sentence planning and surface realization (that is, “translation” of the content suggested by the DM into a natural language utterance that may be spoken using a speech synthesizer or by a human call center agent, or communicated as a text message in an online chat.
The NLG in the dialog scenario can effectively employ a template-based approach, since the content planning (and possibly at least part of the sentence planning) is performed by the DM. In the dialog context, the generated utterances are usually short, e.g. single sentences. So, the focus is on sentence generation rather than complete NLG pipeline for text generation. For these purposes, the template-based approach is well suited. A template-based approach is also effective because the scope of possible utterances to be generated is usually restricted to the specific domain of the dialog. The scope of utterances include greeting the customer, providing/requesting information, instructing the customer, or asking for clarifications among a few other things.
In executing the dialog, the call center agent should sound natural to the customer or client. In constructing a sentence ab initio, even using a sophisticated grammar-based sentence building approach, it is difficult to simulate the subtle aspects of natural human speech, such as the apparently random usage of different paraphrases or synonyms with subtly different shades of meaning. By contrast, template-based approaches disclosed herein capture such language complexity within the template itself, using a conditional probabilistic framework, in order to achieve natural sounding utterances.
In approaches disclosed herein, templates are automatically learned from past dialog training data. Flat templates can be learned from past dialog training data more effectively than a generation grammar. The templates are encoded as weighted finite state automaton (WFSA) structures, which are sometimes referred to herein by the shorthand of a finite state automaton (FSA). These structures enable the capturing of many natural language variations within a single template, as well as enabling the interaction of a template with other templates using the standard FSA operations to generate complex utterances beyond single sentences.
With reference to
With continuing reference to
Agent utterances are typically sentences, although utterances of other types of grammatical units are contemplated (for example, short utterances that are not grammatically correct sentences, or an utterance including two linked sentences such as “So the television is plugged in. Are you sure the electrical outlet is active?”). Since the goal of the utterance generation component (see
With continuing reference to
The induction 26 of word lattices from clusters is performed using multiple sequence alignment (MSA) followed by path scoring and pruning. MSA is an alignment technique commonly used to align genetic sequence read fragments and other bioinformatic sequence fragments. A WFSA is constructed based on these alignments, with paths through the WFSA scored according to how well they align with other paths, and paths with low scores are pruned. The output of the induction 26 is word lattices 30 represented as WFSAs. Path variations classification 32 is then performed on the word lattices 30. The aim of the classification 32 is to identify path variations in the word lattice that correspond to slots, and to identify path variations that correspond to paraphrases (or synonyms for single-word variations). Slots are path variations that will be filled in with (i.e. substituted by) external information provided by a dialog manager. For example, in the utterances “Your television screen size is fifty-five inches” and “Your television screen size is sixty inches”, the difference “fifty-five” and “sixty” are path variations corresponding to slots, since the DA type is to inform the customer/client of the screen size and the particular value “fifty-five” or “sixty” is the slot entry that is filled in from a knowledge base of the dialog manager in constructing a specific utterance recommendation. In general, some slots may be linked to knowledge base concepts, while other slots may have a constrained set of possible fill-in values. The slots labels are incorporated into the word lattices to obtain the utterance generation patterns. This makes the FSA less dense and cleaner for the next step.
In computation 34, conditional probabilities for the word lattice (represented as a WFSA) are computed for given DA types. These probabilities are written herein as p(τ|DA type) where τ is the word lattice/WFSA. In illustrative examples herein, the computation 34 generates the conditional probabilities modelled as a probabilistic transduction operation from the dialog act types (DA type) to the generation patterns (τ), and subsequently to utterances by filling in any slots and performing optional re-ranking based on fluency metrics or the like. To learn this transducer corresponding to the WFSA produced in the earlier steps, a Natural Language Understanding component may be employed to annotate a sample of utterances consumed by the WFSA. From that annotated sampling, the probability given a dialog act type, i.e. p(τ|DA type), is computed. In effect, a WFSA is obtained that maps from dialog act types (DA type) to generation patterns (τ). In the illustrative approach, a WFSA paths sampler 36 generates sample utterances for a word lattice, and these sample utterances are classified by a Natural Language Understanding (NLU) component (or by manual labeling) 38. The statistics p(DA type|utt) for the sample utterances of word lattice τ are then used to generate the probabilities p(τ|DA type) for that word lattice, for example using Bayes' rule.
With reference now to
A dialog manager 50 receives utterances from the customer or client via the call handler or agent station 42. For example, these customer/client utterances may be text utterances in the case of an online chat modality, or spoken utterances transcribed into text utterances by the speech recognition module 18 (
In general, the DA 52 is a recommended semantic-level action (e.g. question or response) to be taken by the call center agent, to then be converted to a natural language utterance that can be conveyed to the second party (e.g. customer or client) via the call modality (e.g. online chat or telephonic). The recommended agent DA 52 can be decomposed into a dialog act type (DA type) 54 and possibly additional data 56 obtained from a knowledge base 58. For example, the DA 52 could be expressed as “TELL(screensize=sixty)” where in this illustrative example the DA type 54 is “TELL(screensize)” and the additional data 56 is sixty (the actual screen size of the customer's television). In another example, the characteristic itself may be additional data for the dialog act type, e.g. the DA type might be “TELL(product·characteristic=value)” where in this example the additional data further includes product·characteristic=screensize. These are merely illustrative examples.
The DA 52, including its constituent DA type 54 and any additional data 56, serves as input to an utterance generation component 60 that applies generation templates 62. The generation templates 62 include the word lattices (represented as WFSAs) and associated conditional probabilities p(τ|DA type) generated by the training system 10 of
In the following, illustrative embodiments of various components of the training and call center systems 10, 40 are described.
The goal of the feature extraction 22 and clustering 24 is to cluster on all the training agent utterances 20 such that sentences in each cluster can be used to get a template. Some suitable criteria for obtaining these clusters include: cohesiveness of dialog act (DA), and surface-level similarity.
Cohesiveness of the DA is addressed in some illustrative embodiments as follows. It is desired that each learned word lattice maps to a particular dialog act (or, more precisely, dialog act type) with good precision. The goal is therefore to obtain clusters that are as cohesive as possible in terms of dialog act, which means that the communicative function (inform, ask, etc.) and the content (devices, carriers, etc.) need to (approximately) match. In order to generate such clusters, two types of cohesiveness-oriented features that may be suitably used are grammatical dependency links and customer/client context. Grammatical dependency links provide grammatical relationships between words within agent utterances, thereby giving hints about both the structure of the sentence (helpful for communicative function), and some relationships that indicate the content. Similarly, an utterance by a customer generates an expectation of the type of response by the agent. For example, if a customer asked a question, the response would less likely be another question. Additionally, if the customer has asked a question about a device, it is expected that the call center agent's utterance would contain some information about the device as well. For the customer context features, n-grams from the customer's utterance may be used.
These two types of features (dependency links and customer/client context) are richer than bag-of-words features in terms of semantic content, and are well-suited for clustering agent's sentences into cohesive dialog acts. In order to generalize on these two classes of cohesiveness-oriented features, dimensionality reduction is optionally applied on each of them independently and then these feature sets are combined before doing the clustering. In one such approach, each of the features is weighted with term frequency-inverse document frequency (tf-idf) scores, and then latent semantic analysis (LSA) is applied for dimensionality reduction, e.g. to 100 dimensions each, after which the two reduced feature sets are weighted using a hyper-parameter and concatenated. In another suitable approach, one can model it as a problem of multi-view learning and apply Canonical Correlation Analysis (CCA) on these two feature sets in order to obtain a feature representation that can be used for clustering.
The word lattice induction also should provide strings that have good surface-level similarity (in addition to semantic similarity). One suitable approach for providing surface-level similarity features is to use n-gram features of a high order (up to 7, or possibly even higher). Typically, it is fair to assume that good surface-level similarity also means a good semantic similarity but is neater to make this division conceptually. Here, LSA is not used in order to concentrate on surface aspects.
With reference to
As further illustrated in
Various clustering algorithms and approaches can be employed for the clustering 24, such as single stage or two-stage clustering. In some demonstrative examples, a single stage strategy was employed using only the surface-level similarity features. In some other demonstrative examples, a two-stage strategy was employed with the first stage using the cohesiveness-oriented features, and then performing sub-clustering using the surface-level features. For the cohesiveness-based grouping, a sentences-per-cluster ratio of 500 was used, while for surface-level similarity based grouping a sentence-per-cluster ratio of 20 was used. The demonstrative examples were performed using k-means clustering where the number of clusters is determined by the sentences-per-cluster-ratio.
With reference to
The utterances of a cluster are preferably labeled as to the DA type, and it is desired that a cluster contain mostly (or entirely) utterances of a single DA type. A metric called “purity” may be used to measure this. For the purity metric, each data point in every cluster is labeled, and the label of the majority of the data points in a cluster is considered as the label of the cluster. That is, the purity of the cluster is computed as:
where φ=ω1, ω2, . . . , ωK is the set of clusters, and C=c1, c2, . . . , cJ is the set of classes. In Expression (1) the notation |ωk ∩cj| represents the number of data points in cluster ωk which are of label cj, and N represents the total number of data points.
For labeling the data points, a baseline Natural Language Understanding (NLU) component may be used, which generates utterances in the cluster with dialog acts. Using a NLU component for computing this metric is not ideal. However, it can still be used to compare various clustering algorithms. As an initial result, baseline purity computed for the clusters obtained using the demonstration one-stage clustering with only surface-level features is 0.69373.
Next, illustrative examples of the word lattice induction 26 of the training of
The word lattice is pruned to remove those paths that are beyond a threshold weight compared to the canonical path (the path with the highest weight or the lowest cost). For example, a threshold of 5 implies that all paths that have at least five differences with the merged parts of highest weighted path are removed. This still permits all the path variations which do not vary much with respect to the merged portions.
With reference to illustrative
Next, illustrative examples of the path variations classification 32 of the training of
With reference to
The word lattice of
The word lattices of
With reference to
where ti is texts and (ti) is the set of groups in which text ti occurs. The Jaccard coefficient operates to avoid favoring frequent texts. The illustrative graph shown in
Next, illustrative examples of the conditional probabilities mapping 34 of word lattices (represented as WFSA's) to dialog act types (DA types) of the training of
The probabilities associated with these decisions are then aggregated to obtain the probabilities of various DA types for the entire WFSA. However, the objective is to obtain a distribution over WFSA's given the logical form. To compute this, Baye's rule can be applied aggregating information over all the WFSA's according to:
where τ is a WFSA, DA type is a dialog act type, p(τ|utt)=0/1, p(utt) represents the probability of seeing an utterance in the dialog transcript, and P(DA type|utt) is the probability of a dialog act type given an utterance which is provided by the Natural Language Understanding component.
The call center device of
In the case of an automatic call center agent, a single utterance is generated by the utterance generation component 60, which is then implemented by a text messenger component (in the case of an automated call center agent communicating by online chat) or speech synthesizer (in a telephonic automated call center agent). To provide (simulated) spontaneity, the single word lattice chosen for instantiation is preferably not necessarily the top-ranked word lattice, but rather the second, third, or possibly even lower-ranked word lattice may occasionally be chosen with some (generally decreasing) probability. Additionally or alternatively, (simulated) spontaneity can be achieved by occasionally traversing other than the highest-probability path in the case of a paraphrase variation, again with some probability that decreases with (e.g. scales with) the weights of the alternative paraphrase paths. If LM or other re-ranking is applied after instantiation, then two or more word lattices that are highest-ranked according to the conditional probabilities p(τ|DA type) may each be instantiated and the highest (or occasionally next-highest) LM-ranked instantiated utterance chosen for implementation by the automated agent.
In the case of a semi-automated call center device, the call center device provides assistance for a human call center agent, for example by listing one, two, or a few suggested utterances on the display component 46. In one approach, one or more highest-ranked word lattices are chosen in accord with the conditional probabilities p(τ|DA type). As just one example, a word lattice may be chosen for instantiation if it is (1) the top-ranked word lattice or (2) the second through fifth ranked word lattice and has conditional probability p(τ|DA type) greater than some minimum selection threshold. (The latter threshold constraint ensures the human agent is not presented with utterances generated from very low-ranked word lattices). In this case all chosen word lattices are preferably instantiated, and optionally are re-ranked using LM fluency ranking or the like, and all chosen word lattices are presented to the human call center agent on the display component 46. In this case, spontaneity is suitably achieved by (1) occasionally traversing other than the highest-probability path in paraphrase variations, and (2) relying upon the human agent to use his or her own spontaneity in selecting which (if any) of the displayed recommended utterances to text-message or articulate to the second party.
The disclosed automated call center system (or semi-automated call center support system) of
In another aspect, the computer implementing the automated or semi-automated call center (support) system is improved by producing more natural-sounding agent utterances (or agent utterance suggestions) with appropriate (simulated) spontaneous variations by leveraging the weighted and pruned word lattices representing training agent utterance clusters, along with the flexibility provided by the slot and paraphrase path variation classes.
Still further, such advantages are achieved while retaining existing dialog manager (DM) paradigms, and indeed leveraging the dialog act (DA) typography employed in such a DM to generate natural-sounding utterances that are appropriate for the particular DA while still having natural-sounding variations from utterance to utterance. The disclosed utterance generation component is also readily integrated with downstream processing such as the use of a language model (LM) to re-rank the utterance suggestions so as to incorporate a fluency metric into the utterance generation.
The training phase (
It will be further appreciated that the computational components disclosed herein may be physically represented by an electronic data processing device (e.g. illustrative devices 12, 44) programmed to perform the disclosed computations; and/or may be physically represented by a non-transitory storage medium storing such instructions executable by an electronic data processing device (e.g. illustrative devices 12, 44) to perform the disclosed computations. Such a non-transitory storage medium may, for example, including one or more of the following non-limiting examples: a hard disk drive or other magnetic storage medium; an optical disk or other optical storage medium; a solid state drive, flash memory, erasable programmable read-only-memory (EPROM) or other electronic storage; various combinations thereof; or so forth.
It should also be noted that while dialogs via online chat or telephone are illustrated, other call modalities are contemplated, such as a video call (which could be fully automated using an agent avatar, or may be performed with a human agent in a video conferencing environment) or email communications modality (where each utterance would be an email message parsed to remove email headers, signatures, or so forth).
It will be appreciated that various of the above-disclosed and other features and functions, or alternatives thereof, may be desirably combined into many other different systems or applications. Also that various presently unforeseen or unanticipated alternatives, modifications, variations or improvements therein may be subsequently made by those skilled in the art which are also intended to be encompassed by the following claims.
Number | Name | Date | Kind |
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6947885 | Bangalore et al. | Sep 2005 | B2 |
7647225 | Bennett | Jan 2010 | B2 |
8849670 | Di Cristo | Sep 2014 | B2 |
9214001 | Rawle | Dec 2015 | B2 |
20060235696 | Bennett | Oct 2006 | A1 |
Entry |
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